NUTA: Non-uniform Temporal Aggregation for Action Recognition

• URL: http://arxiv.org/abs/2012.08041v1
• Date: Tue, 15 Dec 2020 02:03:37 GMT
• Title: NUTA: Non-uniform Temporal Aggregation for Action Recognition
• Authors: Xinyu Li, Chunhui Liu, Bing Shuai, Yi Zhu, Hao Chen, Joseph Tighe
• Abstract summary: We propose a method called the non-uniform temporal aggregation (NUTA), which aggregates features only from informative temporal segments. Our model has achieved state-of-the-art performance on four widely used large-scale action-recognition datasets.
• Score: 29.75987323741384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video. These methods typically uniformly sample a segment of an input clip (along the temporal dimension). However, not all parts of a video are equally important to determine the action in the clip. In this work, we focus instead on learning where to extract features, so as to focus on the most informative parts of the video. We propose a method called the non-uniform temporal aggregation (NUTA), which aggregates features only from informative temporal segments. We also introduce a synchronization method that allows our NUTA features to be temporally aligned with traditional uniformly sampled video features, so that both local and clip-level features can be combined. Our model has achieved state-of-the-art performance on four widely used large-scale action-recognition datasets (Kinetics400, Kinetics700, Something-something V2 and Charades). In addition, we have created a visualization to illustrate how the proposed NUTA method selects only the most relevant parts of a video clip.

Related papers

• Early Action Recognition with Action Prototypes [62.826125870298306]
  We propose a novel model that learns a prototypical representation of the full action for each class. We decompose the video into short clips, where a visual encoder extracts features from each clip independently. Later, a decoder aggregates together in an online fashion features from all the clips for the final class prediction.
  arXiv  Detail & Related papers  (2023-12-11T18:31:13Z)
• CAST: Cross-Attention in Space and Time for Video Action Recognition [8.785207228156098]
  We propose a novel two-stream architecture called Cross-Attention in Space and Time (CAST) CAST achieves a balanced spatial-temporal understanding of videos using only balanced input. Our proposed mechanism enables spatial and temporal expert models to exchange information and make synergistic predictions.
  arXiv  Detail & Related papers  (2023-11-30T18:58:51Z)
• Self-Supervised Video Representation Learning via Latent Time Navigation [12.721647696921865]
  Self-supervised video representation learning aims at maximizing similarity between different temporal segments of one video. We propose Latent Time Navigation (LTN) to capture fine-grained motions. Our experimental analysis suggests that learning video representations by LTN consistently improves performance of action classification.
  arXiv  Detail & Related papers  (2023-05-10T20:06:17Z)
• Spatio-Temporal Crop Aggregation for Video Representation Learning [33.296154476701055]
  Our model builds long-range video features by learning from sets of video clip-level features extracted with a pre-trained backbone. We demonstrate that our video representation yields state-of-the-art performance with linear, non-linear, and $k$-NN probing on common action classification datasets.
  arXiv  Detail & Related papers  (2022-11-30T14:43:35Z)
• Unsupervised Pre-training for Temporal Action Localization Tasks [76.01985780118422]
  We propose a self-supervised pretext task, coined as Pseudo Action localization (PAL) to Unsupervisedly Pre-train feature encoders for Temporal Action localization tasks (UP-TAL) Specifically, we first randomly select temporal regions, each of which contains multiple clips, from one video as pseudo actions and then paste them onto different temporal positions of the other two videos. The pretext task is to align the features of pasted pseudo action regions from two synthetic videos and maximize the agreement between them.
  arXiv  Detail & Related papers  (2022-03-25T12:13:43Z)
• 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)
• 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)
• Efficient Modelling Across Time of Human Actions and Interactions [92.39082696657874]
  We argue that current fixed-sized-temporal kernels in 3 convolutional neural networks (CNNDs) can be improved to better deal with temporal variations in the input. We study how we can better handle between classes of actions, by enhancing their feature differences over different layers of the architecture. The proposed approaches are evaluated on several benchmark action recognition datasets and show competitive results.
  arXiv  Detail & Related papers  (2021-10-05T15:39:11Z)
• Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories [56.91664227337115]
  We introduce a collaborative memory mechanism that encodes information across multiple sampled clips of a video at each training iteration. This enables the learning of long-range dependencies beyond a single clip. Our proposed framework is end-to-end trainable and significantly improves the accuracy of video classification at a negligible computational overhead.
  arXiv  Detail & Related papers  (2021-04-02T18:59:09Z)
• Self-supervised Temporal Discriminative Learning for Video Representation Learning [39.43942923911425]
  Temporal-discriminative features can hardly be extracted without using an annotated large-scale video action dataset for training. This paper proposes a novel Video-based Temporal-Discriminative Learning framework in self-supervised manner.
  arXiv  Detail & Related papers  (2020-08-05T13:36:59Z)

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.