Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition

• URL: http://arxiv.org/abs/2412.11228v1
• Date: Sun, 15 Dec 2024 15:51:44 GMT
• Title: Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition
• Authors: Yulin Wang, Haoji Zhang, Yang Yue, Shiji Song, Chao Deng, Junlan Feng, Gao Huang,
• Abstract summary: This paper investigates the phenomenon of data redundancy in video understanding, with the aim to improve computational efficiency. Motivated by this phenomenon, we introduce a spatially adaptive video recognition approach, termed AdaFocus. Our resulting framework, Uni-AdaFocus, establishes a comprehensive framework that integrates seamlessly spatial, temporal, and sample-wise dynamic computation.
• Score: 82.75714185083383
• License:
• Abstract: This paper presents a comprehensive exploration of the phenomenon of data redundancy in video understanding, with the aim to improve computational efficiency. Our investigation commences with an examination of spatial redundancy, which refers to the observation that the most informative region in each video frame usually corresponds to a small image patch, whose shape, size and location shift smoothly across frames. Motivated by this phenomenon, we formulate the patch localization problem as a dynamic decision task, and introduce a spatially adaptive video recognition approach, termed AdaFocus. In specific, a lightweight encoder is first employed to quickly process the full video sequence, whose features are then utilized by a policy network to identify the most task-relevant regions. Subsequently, the selected patches are inferred by a high-capacity deep network for the final prediction. The full model can be trained in end-to-end conveniently. Furthermore, AdaFocus can be extended by further considering temporal and sample-wise redundancies, i.e., allocating the majority of computation to the most task-relevant frames, and minimizing the computation spent on relatively "easier" videos. Our resulting approach, Uni-AdaFocus, establishes a comprehensive framework that seamlessly integrates spatial, temporal, and sample-wise dynamic computation, while it preserves the merits of AdaFocus in terms of efficient end-to-end training and hardware friendliness. In addition, Uni-AdaFocus is general and flexible as it is compatible with off-the-shelf efficient backbones (e.g., TSM and X3D), which can be readily deployed as our feature extractor, yielding a significantly improved computational efficiency. Empirically, extensive experiments based on seven benchmark datasets and three application scenarios substantiate that Uni-AdaFocus is considerably more efficient than the competitive baselines.

Related papers

• A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design [77.34726150561087]
  We conduct a survey on the most relevant and recent advances in Deep Semantic in the context of vision for autonomous vehicles. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective.
  arXiv  Detail & Related papers  (2023-03-08T01:29:55Z)
• AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition [44.10959567844497]
  This paper explores the unified formulation of spatial-temporal dynamic on top of the recently proposed AdaFocusV2 algorithm. AdaFocusV3 can be effectively trained by approximating the non-differentiable cropping operation with the computation of deep features.
  arXiv  Detail & Related papers  (2022-09-27T15:30:52Z)
• Action Keypoint Network for Efficient Video Recognition [63.48422805355741]
  This paper proposes to integrate temporal and spatial selection into an Action Keypoint Network (AK-Net) AK-Net selects some informative points scattered in arbitrary-shaped regions as a set of action keypoints and then transforms the video recognition into point cloud classification. Experimental results show that AK-Net can consistently improve the efficiency and performance of baseline methods on several video recognition benchmarks.
  arXiv  Detail & Related papers  (2022-01-17T09:35:34Z)
• AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition [23.12743642910384]
  This work reformulates the training of AdaFocus as a simple one-stage algorithm. We present an improved training scheme to address the issues introduced by the one-stage formulation. Our model significantly outperforms the original AdaFocus and other competitive baselines.
  arXiv  Detail & Related papers  (2021-12-28T17:53:38Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild [31.40575057347465]
  We tackle the task of video-based visual emotion recognition in the wild. Standard methodologies that rely solely on the extraction of bodily and facial features often fall short of accurate emotion prediction. We aspire to alleviate this problem by leveraging visual context in the form of scene characteristics and attributes.
  arXiv  Detail & Related papers  (2021-05-16T17:31:59Z)
• Adaptive Focus for Efficient Video Recognition [29.615394426035074]
  We propose a reinforcement learning based approach for efficient spatially adaptive video recognition (AdaFocus) A light-weighted ConvNet is first adopted to quickly process the full video sequence, whose features are used by a recurrent policy network to localize the most task-relevant regions. During offline inference, once the informative patch sequence has been generated, the bulk of computation can be done in parallel, and is efficient on modern GPU devices.
  arXiv  Detail & Related papers  (2021-05-07T13:24:47Z)
• See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks [184.4379622593225]
  We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism. We propose a unified and end-to-end trainable framework where different co-attention variants can be derived for mining the rich context within videos.
  arXiv  Detail & Related papers  (2020-01-19T11:10:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.