DeTAL: Open-Vocabulary Temporal Action Localization with Decoupled Networks.
IEEE Trans Pattern Anal Mach Intell
; PP2024 May 02.
Article
en En
| MEDLINE
| ID: mdl-38696287
ABSTRACT
Pre-trained visual-language (ViL) models have demonstrated good zero-shot capability in video understanding tasks, where they were usually adapted through fine-tuning or temporal modeling. However, in the task of open-vocabulary temporal action localization (OV-TAL), such adaption reduces the robustness of ViL models against different data distributions, leading to a misalignment between visual representations and text descriptions of unseen action categories. As a result, existing methods often strike a trade-off between action detection and classification. Aiming at this issue, this paper proposes DeTAL, a simple but effective two-stage approach for OV-TAL. DeTAL decouples action detection from action classification to avoid the compromise between them, and the state-of-the-art methods for close-set action localization can be handily adapted to OV-TAL, which significantly improves the performance. Meanwhile, DeTAL can easily tackle the scenario where action category annotations are unavailable in the training dataset. In the experiments, we propose a new cross-dataset setting to evaluate the zero-shot capability of different methods. And the results demonstrate that DeTAL outperforms the state-of-the-art methods for OV-TAL on both THUMOS14 and ActivityNet1.3. Code and data are publicly available at https//github.com/vsislab/DeTAL.
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Bases de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Pattern Anal Mach Intell
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article