{"type":"ScholarlyArticle","title":"DTS-TPT: Dual Temporal-Sync Test-time Prompt Tuning for Zero-shot Activity Recognition","authors":[{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Nanjing University"},{"name":"Hongyu Qu","slug":"hongyu-qu","given":"Hongyu","family":"Qu","affiliation":"Nanjing University of Science and Technology"},{"name":"Xiangbo Shu","slug":"xiangbo-shu","given":"Xiangbo","family":"Shu","affiliation":"Nanjing University of Science and Technology"},{"name":"Wenbin Li","slug":"wenbin-li","given":"Wenbin","family":"Li","affiliation":"Nanjing University"},{"name":"Jinhui Tang","slug":"jinhui-tang","given":"Jinhui","family":"Tang","affiliation":"Nanjing University of Science and Technology"},{"name":"Tieniu Tan","slug":"tieniu-tan","given":"Tieniu","family":"Tan","affiliation":"Nanjing University"}],"abstract":"Finetuning the large vision-language models on video data with a set of learnable prompts has shown promising performance on zero-shot activity recognition but still requires extra video data and expensive training costs. Inspired by recent Test-time Prompt Tuning (TPT) on the image domain, this work attempts to extend TPT to video data for zero-shot activity recognition. However, monotonous spatial augmentation and short class names cannot meet the need to capture diverse and complicated semantics of human behavior during prompt tuning. To this end, this work proposes a Dual Temporal-Sync Test-time Prompt Tuning (DTS-TPT) framework for zero-shot activity recognition. DTS-TPT tunes the learnable prompts appended to text inputs on video feature sequences of different temporal scales in multiple steps during test time. In each tuning step, we minimize the semantic consistency among the predictions from video feature sequences randomly augmented via AugMix with both original class names and the corresponding description generated through LLM. Compared with the state-of-the-art methods, the proposed method improves the zero-shot top-1 accuracy by approximately 2% ~ 5% on popular benchmarks. The code is available at https://github.com/quhongyu/DTS-TPT.","doi":"10.24963/ijcai.2024/170","url":"https://ijcai.org/proceedings/2024/170","pdf":"https://ijcai.org/proceedings/2024/0170.pdf","markdown":"https://ijcai.org/proceedings/2024/170.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/170","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":170,"pages":{"first":1534,"last":1542},"keywords":["Computer Vision: CV: Video analysis and understanding","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Azimi et al., 2022] Fatemeh Azimi, Sebastian Palacio, Fed- erico Raue, Jörn Hees, Luca Bertinetto, and Andreas Den- gel. 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