Papers › Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval
Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval
Haokun Wen, Xuemeng Song, Xiaolin Chen, Yinwei Wei, Liqiang Nie, Tat-Seng Chua
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Composed image retrieval (CIR) aims to retrieve the target image based on a multimodal query, i.e., a reference image paired with corresponding modification text. Recent CIR studies leverage vision-language pre-trained (VLP) methods as the feature extraction backbone, and perform nonlinear feature-level multimodal query fusion to retrieve the target image. Despite the promising performance, we argue that their nonlinear feature-level multimodal fusion may lead to the fused feature deviating from the original embedding space, potentially hurting the retrieval performance. To address this issue, in this work, we propose shifting the multimodal fusion from the feature level to the raw-data level to fully exploit the VLP model's multimodal encoding and cross-modal alignment abilities. In particular, we introduce a Dual Query Unification-based Composed Image Retrieval framework (DQU-CIR), whose backbone simply involves a VLP model's image encoder and a text encoder. Specifically, DQU-CIR first employs two training-free query unification components: text-oriented query unification and vision-oriented query unification, to derive a unified textual and visual query based on the raw data of the multimodal query, respectively. The unified textual query is derived by concatenating the modification text with the extracted reference image's textual description, while the unified visual query is created by writing the key modification words onto the reference image. Ultimately, to address diverse search intentions, DQU-CIR linearly combines the features of the two unified queries encoded by the VLP model to retrieve the target image. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections