Abstract
The rapid proliferation of multimedia content has necessitated the development of effective multimodal video retrieval systems. Multimodal video retrieval is a non-trivial task involving retrieval of relevant information across different modalities, such as text, audio, and visual. This work aims to improve multimodal retrieval by guiding the creation of a shared embedding space with task-specific contrastive loss functions. An important aspect of our work is to propose a model that learns retrieval cues for the textual query from multiple modalities both separately and jointly within a hierarchical architecture that can be flexibly extended and fine-tuned for any number of modalities. To this end, the loss functions and the architectural design of the model are developed with a strong focus on increasing the mutual information between the textual and cross-modal representations. The proposed approach is quantitatively evaluated on the MSR-VTT and YouCook2 text-to-video retrieval benchmark datasets. The results showcase that the approach not only holds its own against state-of-the-art methods, but also outperforms them in a number of scenarios, with a notable relative improvements from baseline in R@1, R@5 and R@10 metrics.- Anthology ID:
- 2024.lrec-main.1374
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 15823–15834
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1374
- DOI:
- Cite (ACL):
- Pranav Arora, Selen Pehlivan, and Jorma Laaksonen. 2024. Text-to-Multimodal Retrieval with Bimodal Input Fusion in Shared Cross-Modal Transformer. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15823–15834, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Text-to-Multimodal Retrieval with Bimodal Input Fusion in Shared Cross-Modal Transformer (Arora et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.lrec-main.1374.pdf