Abstract
While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA, to learn an aligned sparse space shared between the video and the text for video-text retrieval. The shared sparse space is initialized with a finite number of sparse concepts, each of which refers to a number of words. With the text data at hand, we learn and update the shared sparse space in a supervised manner using the proposed similarity and alignment losses. Moreover, to enable multi-grained alignment, we incorporate frame representations for better modeling the video modality and calculating fine-grained and coarse-grained similarities. Benefiting from the learned shared sparse space and multi-grained similarities, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of S3MA over existing methods.- Anthology ID:
- 2023.findings-emnlp.46
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 633–649
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.46
- DOI:
- 10.18653/v1/2023.findings-emnlp.46
- Cite (ACL):
- Yimu Wang and Peng Shi. 2023. Video-Text Retrieval by Supervised Sparse Multi-Grained Learning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 633–649, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Video-Text Retrieval by Supervised Sparse Multi-Grained Learning (Wang & Shi, Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-emnlp.46.pdf