The novel task of cross-modal query suggestion is introduced, which interactively guides users by suggesting textual refinements based on visual clusters identified in the retrieval results, and the creation of CroQS, a benchmark dataset comprising 50 diverse queries and 295 semantic clusters in generic domain.
This work proposes a retrieval-time token-aware query reformulation method that emphasizes informative query tokens without retraining or changes to the retriever architecture and constructs a focused query embedding by pooling salient tokens while preserving global semantic context.
Seonah Kim, Minkeon Kim, Youjin Lee et al.· International Conference on...· 0 citations
The Task-Adaptive Hier-archical Prompt (TAHP) framework is proposed, which guides feature extraction through dynamically generated, task-specific prompts structured at three hierarchical levels: task-type, task-content, and general prompts.
Zeli Yan· Poster Volume 0008 The 2026...· 0 citations
This work proposes query-driven image-text joint extraction from long documents, requiring models to output query-requested textual attribute values and corresponding image bounding boxes, and designed a two-level taxonomy that operates at the query and instance levels.
This work introduces L\"etzCross, a benchmark for cross-lingual page-level retrieval over Luxembourgish PDF documents, with document pages indexed as images and queries provided in English, French, German, and Luxembourgish, and examines single-language and multilingual fine-tuning.
Omar El Bachyr, Fred Philippy, L. Bernardy et al.· 0 citations
Given a reference image, composed image retrieval aims to search for the target image from the image gallery with a modification sentence. The complicated semantic relationships within the composed query to comprehend the preserved visual context and modified semantics pose a challenge for the researchers. Most current...
Yifan Wang, Wuliang Huang, Chun Yuan· IEEE Transactions on Image P...· 0 citations
This work proposes an asymmetric fusion mechanism to generate dual retrieval queries of different granularity, enabling the model to fully use multi-modal information and introduces a bi-directional training paradigm to ensure retrieval consistency and further exploit the triplets.
