arXiv Computer Vision
Sketch2Inspire: Structure-Sensitive Evaluation for Product Retrieval
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Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale
The paper introduces GradCIR, a method for training composed image retrieval (CIR) systems on graded relevance rather than binary relevance. It uses a vision‑language model to generate queries and 4‑level relevance labels, an iterative feedback loop to mine hard negatives, and a hierarchy‑aware angular objective to directly optimize graded labels. Experiments on a Walmart catalog and FashionIQ show significant NDCG improvements and the system is deployed in Walmart’s live visual‑search traffic.
Hugging Face Trending Papers
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Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale
Visual search on large e-commerce catalogs must serve both "similarity" queries that ask for items resembling an uploaded image and "modifier" queries that comprise an image and text describing a desi...
arXiv AI
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Generative Universal Multimodal Retrieval with Dual-role Identifiers
arXiv:2608. 12987v1 Announce Type: cross Abstract: Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly.
arXiv AI
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Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs
arXiv:2607. 24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning.
arXiv Computation and Language
Sep 2
AutoConcept: Training-Free Concept-Guided Reranking for Metadata-Available Composed Image Retrieval
arXiv:2609.01456v1 Announce Type: cross Abstract: Composed image retrieval (CIR) retrieves a target image from a reference image and a text modification. This paper studies metadata-available CIR rer...
arXiv AI
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Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment
arXiv:2609.08188v1 Announce Type: new Abstract: Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retriever...