Compressed vision for efficient video understanding. html>xv
Multi-agent reinforcement learning based frame sampling for effective untrimmed video recognition, ICCV 2019. These are stored on a disk and the For MAE, lower is better, for others, higher is better. The paper describes how we can first compress videos to a smaller representation and then train a neural network directly on this compressed representation for various downstream tasks. Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. This is because handling longer videos require more Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. - "Compressed Vision for Efficient Video Understanding" Inspired by recent successes of prompt tuning techniques in computer vision, this paper presents the first attempt to build a prompt based representation learning framework, which enables effective and efficient adaptation of pre-trained raw video models to compressed video understanding tasks. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video Fig. Reproduction of Figure 6 from [66]. Oct 12, 2020 · TLDR. - "Compressed Vision for Efficient Video Understanding" Oct 12, 2020 · Li et al. Related Work Video understanding models aim to parse spatiotempo-ral information in videos. springer. - "Compressed Vision for Efficient Video Understanding" Feb 20, 2024 · Video compression is indispensable to most video analysis systems. 14: How we modify the standard S3D architecture for larger compression rates. Below each layer, we write the size of the output tensor for the given input size. We demonstrate that with our compressed vision pipeline May 25, 2024 · Streaming Long Video Understanding with Large Language Models. Previous ViT pruning methods tend to prune the model along one dimension solely, which may suffer from excessive reduction and lead to Oct 6, 2022 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. - "Compressed Vision for Efficient Video Understanding" Jan 5, 2023 · Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications. . The reason is that feature streams Table 9: Comparison of our pipeline to other methods. com Fig. The input of the TDS-Net is raw RGB video, and the behavioral cues mainly come from the inter-frame difference maps. 9586310. Multi-Attention Network for Compressed Video Referring Object Segmen- Feb 6, 2022 · Our framework can enjoy the best of both two worlds, (1) highly efficient content-coding of industrial video codec and (2) flexible perceptual-coding of neural networks (NNs). Videos are first compressed using a neural compressor 𝑐 to produce codes. Furthermore, through continuous training using The compressed vision pipeline. . This is … In this paper, we have presented a Supertoken Video Transformer, SVT, which employs our proposed semantic pooling module (SPM). Long-term feature banks for detailed video understanding, CVPR 2019. We experiment with different levels of compression (different compression rates (CRs)). 264 efficient for various applications. Source: Action Detection from a Robot-Car Perspective. Finally, we build a rigorous benchmark for compressed video understanding over four different compression levels, six large-scale datasets, and two popular tasks. 2210. Mar 13, 2023 · This paper designs a Cross Resolution Feature Fusion (CR-eFF) module, and supervises it with a novel Feature Similarity Training (FST) strategy to prevent the performance degradation caused by downsampling, and proposes an altering resolution framework for compressed videos to achieve efficient VSS. This shows how the standard S3D architecture is applied to a video. This paper presents a simple but strong baseline to efficiently adapt the pre-trained I-VL model for video understanding Compressed Vision for Efficient Video Understanding. The challenge of video understanding in the Table 3: Downstream classification accuracy on COIN. We demonstrate that with our compressed vision pipeline Sep 15, 2022 · However, the use of SSL for compressed videos has not been an area of focus, and CoVEnPL is the first method that combines SSL and CoViAR. For compression, a scalar quantizer and an entropy coder are utilized to remove redundancy. To the best of our knowledge, this is the first work to address this Previous approaches compress vision tokens with external modules and force LLMs to understand the compressed ones, leading to visual information loss. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for Keywords: Efficient video super-resolution, Compressed video, Codec information assisted, Motion Vectors, Residuals 1 Introduction Compressed videos are prevalent on the Internet, ranging from movies, webcasts to user-generated videos, most of which are of relatively low resolutions and qualities. M. Videosarefirstcompressedusing a neural compressor to produce codes. Action recognition is a crucial task in computer vision and video analysis. Action recognition is different from still image classification; video data contains temporal information that plays an important role in video understanding. 12: S3D. 337 papers with code • 0 benchmarks • 47 datasets. However, thanks to the advances in computer vision systems more and more videos are going to be watched by algorithms, e. implementing video surveillance systems or performing automatic video Dec 5, 2021 · PixelSieve: Towards Efficient Activity Analysis From Compressed Video Streams. However, massive compressed video transmission and analysis require considerable bandwidth and computing resources, posing enormous challenges for current multimedia frameworks. Apr 1, 2023 · In this paper, we present a Supertoken Video Transformer (SVT) that incorporates a Semantic Pooling Module (SPM) to aggregate latent representations along the depth of visual transformer based on Nov 20, 2018 · The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Videos are first compressed using a neural compressor c 𝑐 c to produce codes. The proposed Dual-bitstream Compressed Vision for Efficient Video Understanding. The main contributions of this paper are summarized in four-fold: • We propose CVPT, a novel visual prompt tuning framework, which enables pre-trained raw video models to adapt to compressed video understanding tasks. Conference: 2021 58th ACM/IEEE Design Automation Conference (DAC Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. In this paper, we propose a generic and Oct 8, 2023 · A novel frequency enhancement block for efficient compressed video action recognition, including a temporal-channel two-heads attention (TCTHA) module and a frequency overlapping group convolution (FOGC) module, focusing on the pivotal low-frequency spatio-temporal semantics for action recognition. We demonstrate that with our compressed vision pipeline Jun 29, 2023 · James J. Jan 10, 2024 · SnapCap: Efficient Snapshot Compressive Video Captioning. Videos are first compressed using a neural compressor í µí± to produce codes. However, the LLMs’ understanding paradigm of vision tokens is not fully utilised in the compression learning process. This paper presents a simple but strong baseline to efficiently adapt the pre-trained I-VL model, and exploit its powerful ability for resource-hungry video understanding tasks, with Feb 6, 2022 · Our framework can enjoy the best of both two worlds, (1) highly efficient content-coding of industrial video codec and (2) flexible perceptual-coding of neural networks (NNs). We propose a video captioning method which operates directly on the stored compressed videos. 1: The compressed vision pipeline. It is split into two parts: Initial compression and downstream tasks. The efficiency of this pipeline comes from the fact that once visual data is compressed, it stays compressed through to the end, unlike the standard approach to Mar 11, 2024 · Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. 264, also known as Advanced Video Coding (AVC), is a widely utilized video compression standard. learning technology that is distinct from standard machine learning techniques by its computational. However, in real-world scenarios, the videos are first compressed before the transportation and then decompressed for understanding. A compressed video processing accelerator can remove the decoding overhead, and gain performance speedup by operating on more compact input data. This approach strikes a balance between video quality and file size, making H. I-Frames or Blocks from that representation), a flow (optical flow, motion vectors, or their approximations), or whether the method leverages standard video pipelines (existing popular architectures and augmentations). Although both of them have achieved outstanding performance, the optical flow and 3D convolution require huge computational effort, without taking into account the need for real-time applications. The vast majority of computer vision research, however, still focuses on individual images or short videos and take us one step closer to understanding the interesting long story told by our visual world. - "Compressed Vision for Efficient Video Understanding" Compressed Vision for Efficient Video Understanding Jan 2, 2021 · Existing approaches in video captioning concentrate on exploring global frame features in the uncompressed videos, while the free of charge and critical saliency information already encoded in the compressed videos is generally neglected. Note that we show the result after first applying a space to depth transformation to the input. 2. e Mar 31, 2023 · In summary, our contributions are as follows: We propose LAE-Net, a lightweight and efficient framework, which uses for action recognition tasks in the compressed video domain. Dec 31, 2021 · Multi-Dimensional Model Compression of Vision Transformer. Abstract. Specifically, our method achieves minimal performance loss with a compression ratio of 576 ×, resulting in up to 94. Feb 6, 2022 · Most video understanding methods are learned on high-quality videos. Aug 25, 2023 · Edge computing (EC) is a promising paradigm for serving latency-sensitive video applications. The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it Mar 12, 2024 · H. Experience and reasoning occur across multiple temporal Feb 6, 2022 · Our framework can enjoy the best of both two worlds, (1) highly efficient content-coding of industrial video codec and (2) flexible perceptual-coding of neural networks (NNs). It achieves high accuracy without the computation of optical flow, and finds a tradeoff strategy between computation, parameters, and accuracy. Feb 4, 2023 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. To address this issue, we propose the first coding framework for compressed video understanding, where (TPAMI 2024) VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision Sheng, Xihua and Li, Li and Liu, Dong and Li, Houqiang paper (TPAMI 2024) A Coding Framework and Benchmark towards Low-Bitrate Video Understanding Tian, Yuan and Lu, Guo and Yan, Yichao and Zhai, Guangtao and Chen, Li and Gao, Zhiyong paper Video Understanding. Oct 27, 2022 · In recent years, there have emerged several video understanding-based hand gesture authentication methods. We can also apply augmentations directly in this compressed space, thereby replicating the Recently, convolutional neural networks (CNNs) have seen great progress in classifying images. See full list on link. 6: Learned augmentation: Brightness. Lossless compression experiments show that we significantly improve compression ratios on all types of data: texts, images, videos, and audios. Popular approaches in the past decade include the classic works that use handcrafted fea-tures [12,16,20,36,39,55,75–77], recurrent networks [17, Mar 13, 2024 · This issue is exacerbated by the high-volume video uploads to platforms like YouTube and TikTok, where videos are typically compressed. 48550/arXiv. It adopts an inter-frame compression method, emphasizing the differences between frames to reduce repetition. The top row presents the original video frames, middle row shows rotations whereas the bottom row saturation. We can optionally augment these codes with augmented versions using an augmentation network 𝑎 (here we show a flipping Fig. A dynamic spatial focus method for efficient compressed video action recognition (CoViFocus) using a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors, which reduces the spatial redundancy of the inputs, leading to the high efficiency of the method in the Nov 3, 2022 · 2. 2021. This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for video understanding, that capably understands arbitrary-length video with a constant number of video tokens streamingly encoded and adaptively selected. CR∼1 denotes original RGB frames. Using the neural codes as opposed to the reconstructed images leads to a minor drop in performance ( 1%), demonstrating that improving the quality of the representation would directly improve performance. The core of the temporal shift module is exchanging information between neighbouring frames by moving the feature map along time A generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance and can achieve the performance of 3D CNN but maintain 2D complexity is proposed. , task-decoupled, label-free, and data-emerged semantic efficient representation learning of compressed videos. 4581-4597. To address this challenge, we propose EgoDistill, a distillation-based approach that learns to reconstruct heavy egocentric video clip features by combining the semantics from a sparse set of video frames with the head motion from Aug 10, 2023 · Spatial convolutions are extensively used in numerous deep video models. Jan 1, 2019 · To evaluate the performance of our method, we chose efficient 3DCNNs (such as 3DCNN and MobileNetv2-3D) and the temporal shift module (TSM) [56], a video vision transformer (ViViT) [69], logistic Dec 2, 2017 · 2024. - "Compressed Vision for Efficient Video Understanding" Most video understanding methods are learned on high-quality videos. Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame Video compression algorithms have been designed aiming at pleasing human viewers, and are driven by video quality metrics that are designed to account for the capabilities of the human visual system. Some methods operate on MPEG style representations. 02995 Corpus ID: 252735173; Compressed Vision for Efficient Video Understanding @inproceedings{Wiles2022CompressedVF, title={Compressed Vision for Efficient Video Understanding}, author={Olivia Wiles and Jo{\~a}o F. Jul 26, 2021 · An efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream is proposed and a residual-based correction module is introduced that can fix wrongly propagated segmentation masks from noisy or erroneous motion vectors. Olivia Wiles, Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. model, known as the deep arti ficial neural network, or Abstract. December 2021. The existing frequency-based action recognition methods achieve impressive performance in Sep 19, 2018 · We have introduced an eight-layer deep residual network to extract image features for compression and understanding. 2022. The neural codes are directly used to train video tasks t 1 … t T subscript 𝑡 1 … subscript 𝑡 𝑇 t_{1}\dots t_{T}. In this work, we propose a framework enabling research on hour Oct 17, 2022 · Compressed Vision was made to be an efficient solution for handling visual data for machine learning workflows. Nov 3, 2022 · Deep learning is a type of machine. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. 𝑡𝑇 . (TPAMI 2024) VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision Sheng, Xihua and Li, Li and Liu, Dong and Li, Houqiang paper (TPAMI 2024) A Coding Framework and Benchmark towards Low-Bitrate Video Understanding Tian, Yuan and Lu, Guo and Yan, Yichao and Zhai, Guangtao and Chen, Li and Gao, Zhiyong paper Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. Powered by: Sponsored by: Compressed Vision for Efficient Video Understanding. This article aims to explore the concept of audio compression, its Mar 7, 2014 · This repo contains the code for the ACCV paper on Compressed Vision. However, in such a pipeline, some potential shortcomings are inevitable, i. Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. Jan 10, 2024 · Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. Miller June 29, 2023. e. We propose an efficient plug-and-play acceleration Abstract Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. Fig. DOI: 10. A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video. Currently, most CNN-based approaches for action recognition have excessive computational costs, with an explosion of parameters and Figure 1: The compressed vision pipeline. g. Thus, we introduce the advanced Knowledge Distillation via Knowledge Review (KDKR) to compress the Temporal Difference Symbiotic Neural Network (TDS-Net). Carreira and Iain Barr and Andrew Zisserman and Mateusz Malinowski}, booktitle={Asian Conference on Computer Vision}, year={2022}, url={https://api CR∼1 denotes the upper bound of using the original RGB frames. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. 9: Learned augmentation: Rotations and Saturation. 3. The Two-stream network and 3D ConvNets are representative works. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for “zero-shot” generalisation. These are stored on a disk and the original videos can be discarded. 1109/DAC18074. Thus they Oct 6, 2022 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. TLDR. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i. - "Compressed Vision for Efficient Video Understanding" Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. 2: Augmentation Network. We demonstrate that with our compressed vision pipeline Mar 14, 2023 · To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual ACCV 2022 Open Access Repository. Jun 18, 2024 · VoCo-LLaMA facilitates effective vision compression and improves the computational efficiency during the inference stage. The top row shows the original frames for three videos; the bottom two rows show these frames after applying our equivariant network for brightness at two extremes. As technology has advanced, the need for efficient storage and transmission of audio data has become increasingly important. We propose VoCo-LLaMA, the first approach to compress vision tokens using LLMs. Vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for storage and transmission. Olivia Wiles, Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski. The vast majority of computer vision research, however, still focuses on individual images or short videos lasting only a few seconds. In Lei Wang 0001, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa, editors, Computer Vision - ACCV 2022 - 16th Asian Conference on Computer Vision, Macao, China, December 4-8, 2022, Proceedings, Part VII. SPM can be used with both single-scale and multi-scale transformers to reduce memory and computation requirements as well as improve the performance for video understanding. Jul 11, 2024 · to compress images and videos, retrain an LLM with a small amount of audio data to compress audios, and employ domain-specific finetuned LLMs to compress domain texts. 6 % acceleration in inference time. 8 % fewer FLOPs and 69. To address this issue, we propose the first coding framework for compressed video understanding, where Sep 9, 2023 · Motivated by the success of temporal shift module in efficient video understanding, we adopt this strategy in our network to refine initial reconstructions with the help of temporal correlations among frames. We compare whether each method uses an MPEG style codec (e. 1: The compressed vision pipeline. The decompressed videos may have lost the critical information to the downstream tasks. Extracting pixel data from such compressed videos necessitates full decoding, leading to a storage increase ratio of up to 75:1 for a 1080p30 video compressed at 10 Mbps. Wilesetal. In comparison to Figure 12, we only change the strides of the first convolution, the first three max pools and modify the output channels in the first two convolutional layers. We propose CoVEnPL, which trains models using compressed videos in a semi-supervised Compressed Video Understanding, Vision and Language, Dual-path Dual-attention, Multi-modal Transformer ACM Reference Format: Weidong Chen 1,†, Dexiang Hong,†, Yuankai Qi2, Zhenjun Han1, Shuhui Wang3, 4, Laiyun Qing 1and Qingming Huang,3,, Guorong Li,∗. , using shared weights for every location in different frames. The explosive growth in online video streaming gives rise to challenges on efficiently extracting the spatial-temporal information to perform video understanding. [19] propose a novel Slow-I-Fast-P (SIFP) neural network model for compressed video action recognition. This work proposes a novel deep learning accelerator architecture, Alchemist, which predicts results directly from the compressed video bitstream instead of reconstructing the full RGB images. It consists of the slow I pathway receiving a sparse sampling I-frame clip and the May 7, 2021 · A dynamic spatial focus method for efficient compressed video action recognition (CoViFocus) using a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors, which reduces the spatial redundancy of the inputs, leading to the high efficiency of the method in the compressed domain Sep 21, 2018 · An eight-layer deep residual network is introduced to extract image features for compression and understanding and another residual network-based classifier is patched to perform the classification, with reasonable accuracy at the current stage. We address that by introducing a small network that can apply transformations to latent codes corresponding to commonly used augmentations in the original video space. Conventional 2D CNNs are computationally Dec 8, 2021 · Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for zero-shot generalisation. (a) (b) Fig. By utilizing compressed videos, our training is efficient and easier to scale up than conventional methods. The neural codes are directly used to train video tasks 𝑡1 . It fundamentally assumes spatio-temporal invariance, i. Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The challenge of video understanding in the vision language area mainly lies in the significant MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition, CVPR 2022. Novel multi-stream frameworks that incorporate feature streams are more practical. Compressed Vision for Efficient Video Understanding Olivia Wiles , Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski In Asian Conference on Computer Vision (ACCV), 2022 . Apr 24, 2018 · A network architecture that takes long-term content into account and enables fast per-video processing at the same time and achieves competitive performance across all datasets while being 10 to 80 times faster than state-of-the-art methods. Audio compression is a fundamental aspect of digital audio that plays a crucial role in the context of sound and vision. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate Oct 6, 2022 · We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. Volume 13847 of Lecture Notes in Computer Science, pages 679-695, Springer, 2022. We present a CodedVision framework to achieve image content understanding and compression jointly, leveraging the recent advances in deep neural Feb 6, 2022 · Finally, we build a rigorous benchmark for compressed video understanding over four different compression levels, six large-scale datasets, and two popular tasks. Sharif Digital Repository / Sharif University of Technology : HEVC Compressed Domain Computer Vision,Author: Alizadeh, Mohammad Sadegh,Publisher: Sharif University of Technology , 2019 Mar 31, 2023 · Abstract. Rate-distortion optimization is integrated to improve the coding efficiency where rate is estimated via a piecewise linear approximation. Here, we show other, more challenging transformations. Our method. Iain and Zisserman, Andrew and Malinowski, Mateusz}, title = {Compressed Vision for Efficient Video Oct 6, 2022 · Compressed Vision for Efficient Video Understanding. This is because handling longer videos require more scalable approaches even to process them. We report Top-1 and Top-5 accuracy on COIN when using neural compression trained on either K600 or WalkingTours. However, their parameter number is too large to be deployed directly on mobile devices. The TDS-Net is a customized video understanding model for random hand gesture authentication [ 4 ], which has two branches, the ResNet branch and the Symbiotic branch, respectively. Carreira and Iain Barr and Andrew Zisserman and Mateusz Malinowski}, booktitle={Asian Conference on Computer Vision}, year={2022}, url={https://api - "Compressed Vision for Efficient Video Understanding" Fig. A dynamic spatial focus method for efficient compressed video action recognition (CoViFocus) using a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors, which reduces the spatial redundancy of the inputs, leading to the high efficiency of the method in the compressed Inspired by recent successes of prompt tuning techniques in computer vision, this paper presents the first attempt to build a prompt based representation learning framework, which enables effective and efficient adaptation of pre-trained raw video models to compressed video understanding tasks. We train a network 𝑎 that, conditioned on the bounding box coordinates of the desired spatial crops, performs that spatial crop directly on the latent codes (these embeddings are visualised using PCA). 1 Analyses of the TDS-Net. We demonstrate that with our compressed vision pipeline 2 O. by ci pn rl xv ko se rz fd bp
Multi-agent reinforcement learning based frame sampling for effective untrimmed video recognition, ICCV 2019. These are stored on a disk and the For MAE, lower is better, for others, higher is better. The paper describes how we can first compress videos to a smaller representation and then train a neural network directly on this compressed representation for various downstream tasks. Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. This is because handling longer videos require more Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. - "Compressed Vision for Efficient Video Understanding" Inspired by recent successes of prompt tuning techniques in computer vision, this paper presents the first attempt to build a prompt based representation learning framework, which enables effective and efficient adaptation of pre-trained raw video models to compressed video understanding tasks. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video Fig. Reproduction of Figure 6 from [66]. Oct 12, 2020 · TLDR. - "Compressed Vision for Efficient Video Understanding" Oct 12, 2020 · Li et al. Related Work Video understanding models aim to parse spatiotempo-ral information in videos. springer. - "Compressed Vision for Efficient Video Understanding" Feb 20, 2024 · Video compression is indispensable to most video analysis systems. 14: How we modify the standard S3D architecture for larger compression rates. Below each layer, we write the size of the output tensor for the given input size. We demonstrate that with our compressed vision pipeline May 25, 2024 · Streaming Long Video Understanding with Large Language Models. Previous ViT pruning methods tend to prune the model along one dimension solely, which may suffer from excessive reduction and lead to Oct 6, 2022 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. - "Compressed Vision for Efficient Video Understanding" Jan 5, 2023 · Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications. . The reason is that feature streams Table 9: Comparison of our pipeline to other methods. com Fig. The input of the TDS-Net is raw RGB video, and the behavioral cues mainly come from the inter-frame difference maps. 9586310. Multi-Attention Network for Compressed Video Referring Object Segmen- Feb 6, 2022 · Our framework can enjoy the best of both two worlds, (1) highly efficient content-coding of industrial video codec and (2) flexible perceptual-coding of neural networks (NNs). Videos are first compressed using a neural compressor 𝑐 to produce codes. Furthermore, through continuous training using The compressed vision pipeline. . This is … In this paper, we have presented a Supertoken Video Transformer, SVT, which employs our proposed semantic pooling module (SPM). Long-term feature banks for detailed video understanding, CVPR 2019. We experiment with different levels of compression (different compression rates (CRs)). 264 efficient for various applications. Source: Action Detection from a Robot-Car Perspective. Finally, we build a rigorous benchmark for compressed video understanding over four different compression levels, six large-scale datasets, and two popular tasks. 2210. Mar 13, 2023 · This paper designs a Cross Resolution Feature Fusion (CR-eFF) module, and supervises it with a novel Feature Similarity Training (FST) strategy to prevent the performance degradation caused by downsampling, and proposes an altering resolution framework for compressed videos to achieve efficient VSS. This shows how the standard S3D architecture is applied to a video. This paper presents a simple but strong baseline to efficiently adapt the pre-trained I-VL model for video understanding Compressed Vision for Efficient Video Understanding. The challenge of video understanding in the Table 3: Downstream classification accuracy on COIN. We demonstrate that with our compressed vision pipeline Sep 15, 2022 · However, the use of SSL for compressed videos has not been an area of focus, and CoVEnPL is the first method that combines SSL and CoViAR. For compression, a scalar quantizer and an entropy coder are utilized to remove redundancy. To the best of our knowledge, this is the first work to address this Previous approaches compress vision tokens with external modules and force LLMs to understand the compressed ones, leading to visual information loss. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for Keywords: Efficient video super-resolution, Compressed video, Codec information assisted, Motion Vectors, Residuals 1 Introduction Compressed videos are prevalent on the Internet, ranging from movies, webcasts to user-generated videos, most of which are of relatively low resolutions and qualities. M. Videosarefirstcompressedusing a neural compressor to produce codes. Action recognition is a crucial task in computer vision and video analysis. Action recognition is different from still image classification; video data contains temporal information that plays an important role in video understanding. 12: S3D. 337 papers with code • 0 benchmarks • 47 datasets. However, thanks to the advances in computer vision systems more and more videos are going to be watched by algorithms, e. implementing video surveillance systems or performing automatic video Dec 5, 2021 · PixelSieve: Towards Efficient Activity Analysis From Compressed Video Streams. However, massive compressed video transmission and analysis require considerable bandwidth and computing resources, posing enormous challenges for current multimedia frameworks. Apr 1, 2023 · In this paper, we present a Supertoken Video Transformer (SVT) that incorporates a Semantic Pooling Module (SPM) to aggregate latent representations along the depth of visual transformer based on Nov 20, 2018 · The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Videos are first compressed using a neural compressor c 𝑐 c to produce codes. The proposed Dual-bitstream Compressed Vision for Efficient Video Understanding. The main contributions of this paper are summarized in four-fold: • We propose CVPT, a novel visual prompt tuning framework, which enables pre-trained raw video models to adapt to compressed video understanding tasks. Conference: 2021 58th ACM/IEEE Design Automation Conference (DAC Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. In this paper, we propose a generic and Oct 8, 2023 · A novel frequency enhancement block for efficient compressed video action recognition, including a temporal-channel two-heads attention (TCTHA) module and a frequency overlapping group convolution (FOGC) module, focusing on the pivotal low-frequency spatio-temporal semantics for action recognition. We demonstrate that with our compressed vision pipeline Jun 29, 2023 · James J. Jan 10, 2024 · SnapCap: Efficient Snapshot Compressive Video Captioning. Videos are first compressed using a neural compressor í µí± to produce codes. However, the LLMs’ understanding paradigm of vision tokens is not fully utilised in the compression learning process. This paper presents a simple but strong baseline to efficiently adapt the pre-trained I-VL model, and exploit its powerful ability for resource-hungry video understanding tasks, with Feb 6, 2022 · Our framework can enjoy the best of both two worlds, (1) highly efficient content-coding of industrial video codec and (2) flexible perceptual-coding of neural networks (NNs). We propose a video captioning method which operates directly on the stored compressed videos. 1: The compressed vision pipeline. It is split into two parts: Initial compression and downstream tasks. The efficiency of this pipeline comes from the fact that once visual data is compressed, it stays compressed through to the end, unlike the standard approach to Mar 11, 2024 · Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. 264, also known as Advanced Video Coding (AVC), is a widely utilized video compression standard. learning technology that is distinct from standard machine learning techniques by its computational. However, in real-world scenarios, the videos are first compressed before the transportation and then decompressed for understanding. A compressed video processing accelerator can remove the decoding overhead, and gain performance speedup by operating on more compact input data. This approach strikes a balance between video quality and file size, making H. I-Frames or Blocks from that representation), a flow (optical flow, motion vectors, or their approximations), or whether the method leverages standard video pipelines (existing popular architectures and augmentations). Although both of them have achieved outstanding performance, the optical flow and 3D convolution require huge computational effort, without taking into account the need for real-time applications. The vast majority of computer vision research, however, still focuses on individual images or short videos and take us one step closer to understanding the interesting long story told by our visual world. - "Compressed Vision for Efficient Video Understanding" Compressed Vision for Efficient Video Understanding Jan 2, 2021 · Existing approaches in video captioning concentrate on exploring global frame features in the uncompressed videos, while the free of charge and critical saliency information already encoded in the compressed videos is generally neglected. Note that we show the result after first applying a space to depth transformation to the input. 2. e Mar 31, 2023 · In summary, our contributions are as follows: We propose LAE-Net, a lightweight and efficient framework, which uses for action recognition tasks in the compressed video domain. Dec 31, 2021 · Multi-Dimensional Model Compression of Vision Transformer. Abstract. Specifically, our method achieves minimal performance loss with a compression ratio of 576 ×, resulting in up to 94. Feb 6, 2022 · Most video understanding methods are learned on high-quality videos. Aug 25, 2023 · Edge computing (EC) is a promising paradigm for serving latency-sensitive video applications. The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it Mar 12, 2024 · H. Experience and reasoning occur across multiple temporal Feb 6, 2022 · Our framework can enjoy the best of both two worlds, (1) highly efficient content-coding of industrial video codec and (2) flexible perceptual-coding of neural networks (NNs). It achieves high accuracy without the computation of optical flow, and finds a tradeoff strategy between computation, parameters, and accuracy. Feb 4, 2023 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. To address this issue, we propose the first coding framework for compressed video understanding, where (TPAMI 2024) VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision Sheng, Xihua and Li, Li and Liu, Dong and Li, Houqiang paper (TPAMI 2024) A Coding Framework and Benchmark towards Low-Bitrate Video Understanding Tian, Yuan and Lu, Guo and Yan, Yichao and Zhai, Guangtao and Chen, Li and Gao, Zhiyong paper Video Understanding. Oct 27, 2022 · In recent years, there have emerged several video understanding-based hand gesture authentication methods. We can also apply augmentations directly in this compressed space, thereby replicating the Recently, convolutional neural networks (CNNs) have seen great progress in classifying images. See full list on link. 6: Learned augmentation: Brightness. Lossless compression experiments show that we significantly improve compression ratios on all types of data: texts, images, videos, and audios. Popular approaches in the past decade include the classic works that use handcrafted fea-tures [12,16,20,36,39,55,75–77], recurrent networks [17, Mar 13, 2024 · This issue is exacerbated by the high-volume video uploads to platforms like YouTube and TikTok, where videos are typically compressed. 48550/arXiv. It adopts an inter-frame compression method, emphasizing the differences between frames to reduce repetition. The top row presents the original video frames, middle row shows rotations whereas the bottom row saturation. We can optionally augment these codes with augmented versions using an augmentation network 𝑎 (here we show a flipping Fig. A dynamic spatial focus method for efficient compressed video action recognition (CoViFocus) using a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors, which reduces the spatial redundancy of the inputs, leading to the high efficiency of the method in the Nov 3, 2022 · 2. 2021. This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for video understanding, that capably understands arbitrary-length video with a constant number of video tokens streamingly encoded and adaptively selected. CR∼1 denotes original RGB frames. Using the neural codes as opposed to the reconstructed images leads to a minor drop in performance ( 1%), demonstrating that improving the quality of the representation would directly improve performance. The core of the temporal shift module is exchanging information between neighbouring frames by moving the feature map along time A generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance and can achieve the performance of 3D CNN but maintain 2D complexity is proposed. , task-decoupled, label-free, and data-emerged semantic efficient representation learning of compressed videos. 4581-4597. To address this challenge, we propose EgoDistill, a distillation-based approach that learns to reconstruct heavy egocentric video clip features by combining the semantics from a sparse set of video frames with the head motion from Aug 10, 2023 · Spatial convolutions are extensively used in numerous deep video models. Jan 1, 2019 · To evaluate the performance of our method, we chose efficient 3DCNNs (such as 3DCNN and MobileNetv2-3D) and the temporal shift module (TSM) [56], a video vision transformer (ViViT) [69], logistic Dec 2, 2017 · 2024. - "Compressed Vision for Efficient Video Understanding" Most video understanding methods are learned on high-quality videos. Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame Video compression algorithms have been designed aiming at pleasing human viewers, and are driven by video quality metrics that are designed to account for the capabilities of the human visual system. Some methods operate on MPEG style representations. 02995 Corpus ID: 252735173; Compressed Vision for Efficient Video Understanding @inproceedings{Wiles2022CompressedVF, title={Compressed Vision for Efficient Video Understanding}, author={Olivia Wiles and Jo{\~a}o F. Jul 26, 2021 · An efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream is proposed and a residual-based correction module is introduced that can fix wrongly propagated segmentation masks from noisy or erroneous motion vectors. Olivia Wiles, Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. model, known as the deep arti ficial neural network, or Abstract. December 2021. The existing frequency-based action recognition methods achieve impressive performance in Sep 19, 2018 · We have introduced an eight-layer deep residual network to extract image features for compression and understanding. 2022. The neural codes are directly used to train video tasks t 1 … t T subscript 𝑡 1 … subscript 𝑡 𝑇 t_{1}\dots t_{T}. In this work, we propose a framework enabling research on hour Oct 17, 2022 · Compressed Vision was made to be an efficient solution for handling visual data for machine learning workflows. Nov 3, 2022 · Deep learning is a type of machine. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. 𝑡𝑇 . (TPAMI 2024) VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision Sheng, Xihua and Li, Li and Liu, Dong and Li, Houqiang paper (TPAMI 2024) A Coding Framework and Benchmark towards Low-Bitrate Video Understanding Tian, Yuan and Lu, Guo and Yan, Yichao and Zhai, Guangtao and Chen, Li and Gao, Zhiyong paper Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. Powered by: Sponsored by: Compressed Vision for Efficient Video Understanding. This article aims to explore the concept of audio compression, its Mar 7, 2014 · This repo contains the code for the ACCV paper on Compressed Vision. However, in such a pipeline, some potential shortcomings are inevitable, i. Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. Jan 10, 2024 · Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. Miller June 29, 2023. e. We propose an efficient plug-and-play acceleration Abstract Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. Fig. DOI: 10. A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video. Currently, most CNN-based approaches for action recognition have excessive computational costs, with an explosion of parameters and Figure 1: The compressed vision pipeline. g. Thus, we introduce the advanced Knowledge Distillation via Knowledge Review (KDKR) to compress the Temporal Difference Symbiotic Neural Network (TDS-Net). Carreira and Iain Barr and Andrew Zisserman and Mateusz Malinowski}, booktitle={Asian Conference on Computer Vision}, year={2022}, url={https://api CR∼1 denotes the upper bound of using the original RGB frames. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. 9: Learned augmentation: Rotations and Saturation. 3. The Two-stream network and 3D ConvNets are representative works. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for “zero-shot” generalisation. These are stored on a disk and the original videos can be discarded. 1109/DAC18074. Thus they Oct 6, 2022 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. TLDR. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i. - "Compressed Vision for Efficient Video Understanding" Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. 2: Augmentation Network. We demonstrate that with our compressed vision pipeline Mar 14, 2023 · To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual ACCV 2022 Open Access Repository. Jun 18, 2024 · VoCo-LLaMA facilitates effective vision compression and improves the computational efficiency during the inference stage. The top row shows the original frames for three videos; the bottom two rows show these frames after applying our equivariant network for brightness at two extremes. As technology has advanced, the need for efficient storage and transmission of audio data has become increasingly important. We propose VoCo-LLaMA, the first approach to compress vision tokens using LLMs. Vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for storage and transmission. Olivia Wiles, Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski. The vast majority of computer vision research, however, still focuses on individual images or short videos lasting only a few seconds. In Lei Wang 0001, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa, editors, Computer Vision - ACCV 2022 - 16th Asian Conference on Computer Vision, Macao, China, December 4-8, 2022, Proceedings, Part VII. SPM can be used with both single-scale and multi-scale transformers to reduce memory and computation requirements as well as improve the performance for video understanding. Jul 11, 2024 · to compress images and videos, retrain an LLM with a small amount of audio data to compress audios, and employ domain-specific finetuned LLMs to compress domain texts. 6 % acceleration in inference time. 8 % fewer FLOPs and 69. To address this issue, we propose the first coding framework for compressed video understanding, where Sep 9, 2023 · Motivated by the success of temporal shift module in efficient video understanding, we adopt this strategy in our network to refine initial reconstructions with the help of temporal correlations among frames. We compare whether each method uses an MPEG style codec (e. 1: The compressed vision pipeline. The decompressed videos may have lost the critical information to the downstream tasks. Extracting pixel data from such compressed videos necessitates full decoding, leading to a storage increase ratio of up to 75:1 for a 1080p30 video compressed at 10 Mbps. Wilesetal. In comparison to Figure 12, we only change the strides of the first convolution, the first three max pools and modify the output channels in the first two convolutional layers. We propose CoVEnPL, which trains models using compressed videos in a semi-supervised Compressed Video Understanding, Vision and Language, Dual-path Dual-attention, Multi-modal Transformer ACM Reference Format: Weidong Chen 1,†, Dexiang Hong,†, Yuankai Qi2, Zhenjun Han1, Shuhui Wang3, 4, Laiyun Qing 1and Qingming Huang,3,, Guorong Li,∗. , using shared weights for every location in different frames. The explosive growth in online video streaming gives rise to challenges on efficiently extracting the spatial-temporal information to perform video understanding. [19] propose a novel Slow-I-Fast-P (SIFP) neural network model for compressed video action recognition. This work proposes a novel deep learning accelerator architecture, Alchemist, which predicts results directly from the compressed video bitstream instead of reconstructing the full RGB images. It consists of the slow I pathway receiving a sparse sampling I-frame clip and the May 7, 2021 · A dynamic spatial focus method for efficient compressed video action recognition (CoViFocus) using a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors, which reduces the spatial redundancy of the inputs, leading to the high efficiency of the method in the compressed domain Sep 21, 2018 · An eight-layer deep residual network is introduced to extract image features for compression and understanding and another residual network-based classifier is patched to perform the classification, with reasonable accuracy at the current stage. We address that by introducing a small network that can apply transformations to latent codes corresponding to commonly used augmentations in the original video space. Conventional 2D CNNs are computationally Dec 8, 2021 · Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for zero-shot generalisation. (a) (b) Fig. By utilizing compressed videos, our training is efficient and easier to scale up than conventional methods. The neural codes are directly used to train video tasks 𝑡1 . It fundamentally assumes spatio-temporal invariance, i. Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The challenge of video understanding in the vision language area mainly lies in the significant MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition, CVPR 2022. Novel multi-stream frameworks that incorporate feature streams are more practical. Compressed Vision for Efficient Video Understanding Olivia Wiles , Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski In Asian Conference on Computer Vision (ACCV), 2022 . Apr 24, 2018 · A network architecture that takes long-term content into account and enables fast per-video processing at the same time and achieves competitive performance across all datasets while being 10 to 80 times faster than state-of-the-art methods. Audio compression is a fundamental aspect of digital audio that plays a crucial role in the context of sound and vision. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate Oct 6, 2022 · We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. Volume 13847 of Lecture Notes in Computer Science, pages 679-695, Springer, 2022. We present a CodedVision framework to achieve image content understanding and compression jointly, leveraging the recent advances in deep neural Feb 6, 2022 · Finally, we build a rigorous benchmark for compressed video understanding over four different compression levels, six large-scale datasets, and two popular tasks. Sharif Digital Repository / Sharif University of Technology : HEVC Compressed Domain Computer Vision,Author: Alizadeh, Mohammad Sadegh,Publisher: Sharif University of Technology , 2019 Mar 31, 2023 · Abstract. Rate-distortion optimization is integrated to improve the coding efficiency where rate is estimated via a piecewise linear approximation. Here, we show other, more challenging transformations. Our method. Iain and Zisserman, Andrew and Malinowski, Mateusz}, title = {Compressed Vision for Efficient Video Oct 6, 2022 · Compressed Vision for Efficient Video Understanding. This is because handling longer videos require more scalable approaches even to process them. We report Top-1 and Top-5 accuracy on COIN when using neural compression trained on either K600 or WalkingTours. However, their parameter number is too large to be deployed directly on mobile devices. The TDS-Net is a customized video understanding model for random hand gesture authentication [ 4 ], which has two branches, the ResNet branch and the Symbiotic branch, respectively. Carreira and Iain Barr and Andrew Zisserman and Mateusz Malinowski}, booktitle={Asian Conference on Computer Vision}, year={2022}, url={https://api - "Compressed Vision for Efficient Video Understanding" Fig. A dynamic spatial focus method for efficient compressed video action recognition (CoViFocus) using a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors, which reduces the spatial redundancy of the inputs, leading to the high efficiency of the method in the compressed Inspired by recent successes of prompt tuning techniques in computer vision, this paper presents the first attempt to build a prompt based representation learning framework, which enables effective and efficient adaptation of pre-trained raw video models to compressed video understanding tasks. We train a network 𝑎 that, conditioned on the bounding box coordinates of the desired spatial crops, performs that spatial crop directly on the latent codes (these embeddings are visualised using PCA). 1 Analyses of the TDS-Net. We demonstrate that with our compressed vision pipeline 2 O. by ci pn rl xv ko se rz fd bp