Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition

• URL: http://arxiv.org/abs/2403.01560v2
• Date: Fri, 24 May 2024 14:47:03 GMT
• Title: Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition
• Authors: Kun-Yu Lin, Henghui Ding, Jiaming Zhou, Yu-Ming Tang, Yi-Xing Peng, Zhilin Zhao, Chen Change Loy, Wei-Shi Zheng,
• Abstract summary: In this paper, we focus on one critical challenge of the task, namely scene bias, and accordingly contribute a novel scene-aware video-text alignment method. Our key idea is to distinguish video representations apart from scene-encoded text representations, aiming to learn scene-agnostic video representations for recognizing actions across domains.
• Score: 84.31749632725929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building upon the impressive success of CLIP (Contrastive Language-Image Pretraining), recent pioneer works have proposed to adapt the powerful CLIP to video data, leading to efficient and effective video learners for open-vocabulary action recognition. Inspired by that humans perform actions in diverse environments, our work delves into an intriguing question: Can CLIP-based video learners effectively generalize to video domains they have not encountered during training? To answer this, we establish a CROSS-domain Open-Vocabulary Action recognition benchmark named XOV-Action, and conduct a comprehensive evaluation of five state-of-the-art CLIP-based video learners under various types of domain gaps. The evaluation demonstrates that previous methods exhibit limited action recognition performance in unseen video domains, revealing potential challenges of the cross-domain open-vocabulary action recognition task. In this paper, we focus on one critical challenge of the task, namely scene bias, and accordingly contribute a novel scene-aware video-text alignment method. Our key idea is to distinguish video representations apart from scene-encoded text representations, aiming to learn scene-agnostic video representations for recognizing actions across domains. Extensive experiments demonstrate the effectiveness of our method. The benchmark and code will be available at https://github.com/KunyuLin/XOV-Action/.

Related papers

• Open-Vocabulary Action Localization with Iterative Visual Prompting [8.07285448283823]
  Video action localization aims to find the timings of specific actions from a long video. This paper proposes a learning-free, open-vocabulary approach based on emerging vision-language models.
  arXiv  Detail & Related papers  (2024-08-30T17:12:14Z)
• Semi-supervised Active Learning for Video Action Detection [8.110693267550346]
  We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data. We evaluate the proposed approach on three different benchmark datasets, UCF-24-101, JHMDB-21, and Youtube-VOS.
  arXiv  Detail & Related papers  (2023-12-12T11:13:17Z)
• Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data [102.0069667710562]
  This paper presents Open-VCLIP++, a framework that adapts CLIP to a strong zero-shot video classifier. We demonstrate that training Open-VCLIP++ is tantamount to continual learning with zero historical data. Our approach is evaluated on three widely used action recognition datasets.
  arXiv  Detail & Related papers  (2023-10-08T04:46:43Z)
• Learning video embedding space with Natural Language Supervision [1.6822770693792823]
  We propose a novel approach to map video embedding space to natural langugage. We propose a two-stage approach that first extracts visual features from each frame of a video using a pre-trained CNN, and then uses the CLIP model to encode the visual features for the video domain.
  arXiv  Detail & Related papers  (2023-03-25T23:24:57Z)
• Visual Commonsense-aware Representation Network for Video Captioning [84.67432867555044]
  We propose a simple yet effective method, called Visual Commonsense-aware Representation Network (VCRN) for video captioning. Our method reaches state-of-the-art performance, indicating the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-11-17T11:27:15Z)
• Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework [108.70949305791201]
  Part-level Action Parsing (PAP) aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.
  arXiv  Detail & Related papers  (2022-03-09T01:30:57Z)
• Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts [89.06560404218028]
  We introduce a new method for pre-training video action recognition models using queried web videos. Instead of trying to filter out, we propose to convert the potential noises in these queried videos to useful supervision signals. We show that SPL outperforms several existing pre-training strategies using pseudo-labels.
  arXiv  Detail & Related papers  (2021-01-11T05:50:16Z)
• 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.