When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning

• URL: http://arxiv.org/abs/2503.15096v1
• Date: Wed, 19 Mar 2025 10:50:03 GMT
• Title: When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning
• Authors: Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai, Qingming Huang,
• Abstract summary: We propose a self-supervised framework that leverages Temporal Correspondence for video representation learning (T-CoRe)<n>Experiments of T-CoRe consistently present superior performance across several downstream tasks, demonstrating its effectiveness for video representation learning.
• Score: 80.09819072780193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The past decade has witnessed notable achievements in self-supervised learning for video tasks. Recent efforts typically adopt the Masked Video Modeling (MVM) paradigm, leading to significant progress on multiple video tasks. However, two critical challenges remain: 1) Without human annotations, the random temporal sampling introduces uncertainty, increasing the difficulty of model training. 2) Previous MVM methods primarily recover the masked patches in the pixel space, leading to insufficient information compression for downstream tasks. To address these challenges jointly, we propose a self-supervised framework that leverages Temporal Correspondence for video Representation learning (T-CoRe). For challenge 1), we propose a sandwich sampling strategy that selects two auxiliary frames to reduce reconstruction uncertainty in a two-side-squeezing manner. Addressing challenge 2), we introduce an auxiliary branch into a self-distillation architecture to restore representations in the latent space, generating high-level semantic representations enriched with temporal information. Experiments of T-CoRe consistently present superior performance across several downstream tasks, demonstrating its effectiveness for video representation learning. The code is available at https://github.com/yafeng19/T-CORE.

Related papers

• 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)
• DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation [37.25815760042241]
  This paper introduces a new framework, dubbed DirecT2V, to generate text-to-video (T2V) videos. We equip a diffusion model with a novel value mapping method and dual-softmax filtering, which do not require any additional training. The experimental results validate the effectiveness of our framework in producing visually coherent and storyful videos.
  arXiv  Detail & Related papers  (2023-05-23T17:57:09Z)
• Transform-Equivariant Consistency Learning for Temporal Sentence Grounding [66.10949751429781]
  We introduce a novel Equivariant Consistency Regulation Learning framework to learn more discriminative representations for each video. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted. In particular, we devise a self-supervised consistency loss module to enhance the completeness and smoothness of the augmented video.
  arXiv  Detail & Related papers  (2023-05-06T19:29:28Z)
• Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detection [14.721615285883423]
  Weakly supervised anomaly detection (WS-VAD) is a challenging problem that aims to learn VAD models only with video-level annotations. Our proposed method is able to better deal with anomalies with varying durations as well as subtle anomalies.
  arXiv  Detail & Related papers  (2023-03-31T13:28:06Z)
• Unmasked Teacher: Towards Training-Efficient Video Foundation Models [50.19560876891811]
  Video Foundation Models (VFMs) have received limited exploration due to high computational costs and data scarcity. This paper proposes a training-efficient method for temporal-sensitive VFMs that integrates the benefits of existing methods. Our model can handle various tasks including scene-related, temporal-related, and complex video-language understanding.
  arXiv  Detail & Related papers  (2023-03-28T15:39:28Z)
• Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations [26.09611987412578]
  We introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner. Specifically, we introduce a simple but effective video encoder that considers both spatial and temporal context. Our method outperforms previous state-of-the-art by a large margin for downstream fine-grained action classification and even faster inference.
  arXiv  Detail & Related papers  (2022-12-06T16:42:22Z)
• Masked Motion Encoding for Self-Supervised Video Representation Learning [84.24773072241945]
  We present Masked Motion MME, a new pre-training paradigm that reconstructs both appearance and motion information to explore temporal clues. Motivated by the fact that human is able to recognize an action by tracking objects' position changes and shape changes, we propose to reconstruct a motion trajectory that represents these two kinds of change in the masked regions. Pre-trained with our MME paradigm, the model is able to anticipate long-term and fine-grained motion details.
  arXiv  Detail & Related papers  (2022-10-12T11:19:55Z)
• ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency [62.38914747727636]
  We study self-supervised video representation learning, which is a challenging task due to 1) a lack of labels for explicit supervision and 2) unstructured and noisy visual information. Existing methods mainly use contrastive loss with video clips as the instances and learn visual representation by discriminating instances from each other. In this paper, we observe that the consistency between positive samples is the key to learn robust video representations.
  arXiv  Detail & Related papers  (2021-06-04T08:44:50Z)

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.