POTLoc: Pseudo-Label Oriented Transformer for Point-Supervised Temporal Action Localization

• URL: http://arxiv.org/abs/2310.13585v2
• Date: Wed, 5 Jun 2024 18:31:34 GMT
• Title: POTLoc: Pseudo-Label Oriented Transformer for Point-Supervised Temporal Action Localization
• Authors: Elahe Vahdani, Yingli Tian,
• Abstract summary: This paper proposes POTLoc, a Pseudo-label Oriented Transformer for weakly-supervised Action localization. POTLoc is designed to identify and track continuous action structures via a self-training strategy. It outperforms the state-of-the-art point-supervised methods on THUMOS'14 and ActivityNet-v1.2 datasets.
• Score: 26.506893363676678
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper tackles the challenge of point-supervised temporal action detection, wherein only a single frame is annotated for each action instance in the training set. Most of the current methods, hindered by the sparse nature of annotated points, struggle to effectively represent the continuous structure of actions or the inherent temporal and semantic dependencies within action instances. Consequently, these methods frequently learn merely the most distinctive segments of actions, leading to the creation of incomplete action proposals. This paper proposes POTLoc, a Pseudo-label Oriented Transformer for weakly-supervised Action Localization utilizing only point-level annotation. POTLoc is designed to identify and track continuous action structures via a self-training strategy. The base model begins by generating action proposals solely with point-level supervision. These proposals undergo refinement and regression to enhance the precision of the estimated action boundaries, which subsequently results in the production of `pseudo-labels' to serve as supplementary supervisory signals. The architecture of the model integrates a transformer with a temporal feature pyramid to capture video snippet dependencies and model actions of varying duration. The pseudo-labels, providing information about the coarse locations and boundaries of actions, assist in guiding the transformer for enhanced learning of action dynamics. POTLoc outperforms the state-of-the-art point-supervised methods on THUMOS'14 and ActivityNet-v1.2 datasets.

Related papers

• STAT: Towards Generalizable Temporal Action Localization [56.634561073746056]
  Weakly-supervised temporal action localization (WTAL) aims to recognize and localize action instances with only video-level labels. Existing methods suffer from severe performance degradation when transferring to different distributions. We propose GTAL, which focuses on improving the generalizability of action localization methods.
  arXiv  Detail & Related papers  (2024-04-20T07:56:21Z)
• ADM-Loc: Actionness Distribution Modeling for Point-supervised Temporal Action Localization [31.314383098734922]
  This paper addresses the challenge of point-supervised temporal action detection, in which only one frame per action instance is annotated in the training set. It proposes a novel framework termed ADM-Loc, which stands for Actionness Distribution Modeling for point-supervised action localization.
  arXiv  Detail & Related papers  (2023-11-27T15:24:54Z)
• Proposal-based Temporal Action Localization with Point-level Supervision [29.98225940694062]
  Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos. We propose a novel method that localizes actions by generating and evaluating action proposals of flexible duration. Experiments show that our proposed method achieves competitive or superior performance to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-09T08:27:05Z)
• Sub-action Prototype Learning for Point-level Weakly-supervised Temporal Action Localization [11.777205793663647]
  Point-level weakly-supervised temporal action localization (PWTAL) aims to localize actions with only a single timestamp annotation for each action instance. Existing methods tend to mine dense pseudo labels to alleviate the label sparsity, but overlook the potential sub-action temporal structures, resulting in inferior performance. We propose a novel sub-action prototype learning framework (SPL-Loc) which comprises Sub-action Prototype Clustering (SPC) and Ordered Prototype Alignment (OPA)
  arXiv  Detail & Related papers  (2023-09-16T17:57:40Z)
• ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization [36.90693762365237]
  Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. We propose system, a novel WTAL framework that enables explicit, action-aware segment modeling beyond standard MIL-based methods. Our framework entails three segment-centric components: (i) dynamic segment sampling for compensating the contribution of short actions; (ii) intra- and inter-segment attention for modeling action dynamics and capturing temporal dependencies; (iii) pseudo instance-level supervision for improving action boundary prediction.
  arXiv  Detail & Related papers  (2022-03-29T01:59:26Z)
• Transformers in Action:Weakly Supervised Action Segmentation [81.18941007536468]
  We show how to apply transformers to improve action alignment accuracy over the equivalent RNN-based models. We also propose a supplemental transcript embedding approach to select transcripts more quickly at inference-time. We evaluate our proposed methods across the benchmark datasets to better understand the applicability of transformers.
  arXiv  Detail & Related papers  (2022-01-14T21:15:58Z)
• Background-Click Supervision for Temporal Action Localization [82.4203995101082]
  Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion. One recent work builds an action-click supervision framework. It requires similar annotation costs but can steadily improve the localization performance when compared to the conventional weakly supervised methods. In this paper, by revealing that the performance bottleneck of the existing approaches mainly comes from the background errors, we find that a stronger action localizer can be trained with labels on the background video frames rather than those on the action frames.
  arXiv  Detail & Related papers  (2021-11-24T12:02:52Z)
• Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation [79.98992138865042]
  We present an augmented transformer with adaptive graph network (ATAG) to exploit both long-range and local temporal contexts for TAPG. Specifically, we enhance the vanilla transformer by equipping a snippet actionness loss and a front block, dubbed augmented transformer. An adaptive graph convolutional network (GCN) is proposed to build local temporal context by mining the position information and difference between adjacent features.
  arXiv  Detail & Related papers  (2021-03-30T02:01:03Z)
• Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses [84.2964408497058]
  Point-level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the frame-level prediction paradigm to learn from the sparse single-frame labels. This paper attempts to explore the proposal-based prediction paradigm for point-level annotations.
  arXiv  Detail & Related papers  (2020-12-15T12:11:48Z)
• Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization [94.37084866660238]
  We present a Two-Stream Consensus Network (TSCN) to simultaneously address these challenges. The proposed TSCN features an iterative refinement training method, where a frame-level pseudo ground truth is iteratively updated. We propose a new attention normalization loss to encourage the predicted attention to act like a binary selection, and promote the precise localization of action instance boundaries.
  arXiv  Detail & Related papers  (2020-10-22T10:53:32Z)

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.