2025-01-14-wang25d.md

title	booktitle	year	volume	series	month	publisher	pdf	url	openreview	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Prompting vision-language fusion for Zero-Shot Composed Image Retrieval	Proceedings of the 16th Asian Conference on Machine Learning	2025	260	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v260/main/assets/wang25d/wang25d.pdf	https://proceedings.mlr.press/v260/wang25d.html	8NM3qmWjkt	The composed image retrieval (CIR) aims to retrieve target image given the combination of an image and a textual description as a query. Recently, benefiting from vision-language pretrained (VLP) models and large language models (LLM), the use of textual inversion or generating large-scale datasets has become a novel approach for zero-shot CIR task (ZS-CIR). However, the existing ZS-CIR models overlook one case where the textual description is often too brief or inherently inaccurate, making it challenging to effectively integrate the reference image into the query for retrieving the target image. To address this problem, we propose a simple yet effective method—prompting vision-language fusion (PVLF), which adapts representations in VLP models to dynamically fuse the vision and language (V&L) representation spaces. In addition, by injecting the context learnable prompt tokens in Transformer fusion encoder, the PVLF promotes the comprehensive coupling between V&L modalities, enriching the semantic representation of the query. We evaluate the effectiveness and robustness of our method on various VLP backbones, and the experimental results show that the proposed PVLF outperforms previous methods and achieves the state-of-the-art on two public ZS-CIR benchmarks (CIRR and FashionIQ).	inproceedings	2640-3498	wang25d	Prompting vision-language fusion for Zero-Shot Composed Image Retrieval	671	686	671-686	671	false	Nguyen, Vu and Lin, Hsuan-Tien	given family Vu Nguyen given family Hsuan-Tien Lin	Wang, Peng and Chen, Zining and Zhao, Zhicheng and Su, Fei	given family Peng Wang given family Zining Chen given family Zhicheng Zhao given family Fei Su	2025-01-14		Proceedings of the 16th Asian Conference on Machine Learning	inproceedings	date-parts 2025 1 14