Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity

• URL: http://arxiv.org/abs/2112.05883v1
• Date: Sat, 11 Dec 2021 00:35:27 GMT
• Title: Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity
• Authors: Hanwen Liang, Niamul Quader, Zhixiang Chi, Lizhe Chen, Peng Dai, Juwei Lu, Yang Wang
• Abstract summary: This work exploits an essential yet under-explored property of videos, the textitvideo continuity, to obtain supervision signals for self-supervised representation learning. We formulate three novel continuity-related pretext tasks, i.e. continuity justification, discontinuity localization, and missing section approximation. This self-supervision approach, termed as Continuity Perception Network (CPNet), solves the three tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations.
• Score: 15.429045937335236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. This work exploits an essential yet under-explored property of videos, the \textit{video continuity}, to obtain supervision signals for self-supervised representation learning. Specifically, we formulate three novel continuity-related pretext tasks, i.e. continuity justification, discontinuity localization, and missing section approximation, that jointly supervise a shared backbone for video representation learning. This self-supervision approach, termed as Continuity Perception Network (CPNet), solves the three tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations. It outperforms prior arts on multiple downstream tasks, such as action recognition, video retrieval, and action localization. Additionally, the video continuity can be complementary to other coarse-grained video properties for representation learning, and integrating the proposed pretext task to prior arts can yield much performance gains.

Related papers

• Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases [69.46487306858789]
  Conditional Autoregressive Slot Attention (CA-SA) is a framework that enhances the temporal consistency of extracted object-centric representations in video-centric vision tasks. We present qualitative and quantitative results showing that our proposed method outperforms the considered baselines on downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T07:44:44Z)
• Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding [70.31050639330603]
  Video paragraph grounding aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. Existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. We introduce and explore Weakly-Supervised Video paragraph Grounding (WSVPG) to eliminate the need of temporal annotations.
  arXiv  Detail & Related papers  (2024-03-18T04:30:31Z)
• No More Shortcuts: Realizing the Potential of Temporal Self-Supervision [69.59938105887538]
  We propose a more challenging reformulation of temporal self-supervision as frame-level (rather than clip-level) recognition tasks. We demonstrate experimentally that our more challenging frame-level task formulations and the removal of shortcuts drastically improve the quality of features learned through temporal self-supervision.
  arXiv  Detail & Related papers  (2023-12-20T13:20:31Z)
• TAEC: Unsupervised Action Segmentation with Temporal-Aware Embedding and Clustering [27.52568444236988]
  We propose an unsupervised approach for learning action classes from untrimmed video sequences. In particular, we propose a temporal embedding network that combines relative time prediction, feature reconstruction, and sequence-to-sequence learning. Based on the identified clusters, we decode the video into coherent temporal segments that correspond to semantically meaningful action classes.
  arXiv  Detail & Related papers  (2023-03-09T10:46:23Z)
• SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video [61.21388780334379]
  This work focuses on the apparent emotional reaction recognition from the video-only input, conducted in a self-supervised fashion. The network is first pre-trained on different self-supervised pretext tasks and later fine-tuned on the downstream target task.
  arXiv  Detail & Related papers  (2022-10-20T15:21:51Z)
• Boundary-aware Self-supervised Learning for Video Scene Segmentation [20.713635723315527]
  Video scene segmentation is a task of temporally localizing scene boundaries in a video. We introduce three novel boundary-aware pretext tasks: Shot-Scene Matching, Contextual Group Matching and Pseudo-boundary Prediction. We achieve the new state-of-the-art on the MovieNet-SSeg benchmark.
  arXiv  Detail & Related papers  (2022-01-14T02:14:07Z)
• Self-Regulated Learning for Egocentric Video Activity Anticipation [147.9783215348252]
  Self-Regulated Learning (SRL) aims to regulate the intermediate representation consecutively to produce representation that emphasizes the novel information in the frame of the current time-stamp. SRL sharply outperforms existing state-of-the-art in most cases on two egocentric video datasets and two third-person video datasets.
  arXiv  Detail & Related papers  (2021-11-23T03:29:18Z)
• Learning Actor-centered Representations for Action Localization in Streaming Videos using Predictive Learning [18.757368441841123]
  Event perception tasks such as recognizing and localizing actions in streaming videos are essential for tackling visual understanding tasks. We tackle the problem of learning textitactor-centered representations through the notion of continual hierarchical predictive learning. Inspired by cognitive theories of event perception, we propose a novel, self-supervised framework.
  arXiv  Detail & Related papers  (2021-04-29T06:06:58Z)
• Unsupervised Learning of Video Representations via Dense Trajectory Clustering [86.45054867170795]
  This paper addresses the task of unsupervised learning of representations for action recognition in videos. We first propose to adapt two top performing objectives in this class - instance recognition and local aggregation. We observe promising performance, but qualitative analysis shows that the learned representations fail to capture motion patterns.
  arXiv  Detail & Related papers  (2020-06-28T22:23:03Z)

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.