CORE improves compositional reasoning in multimodal language models by distilling a cross‑attentive reranker’s fine‑grained judgments into the embedding model. It generates candidate lists across five compositional matching levels and trains with a Rank‑KL objective to replicate the reranker’s ranking. Experiments on COLA, SUGARCREPE++, and NEGBENCH show CORE‑RERANKER‑8B outperforms Jina‑Reranker by 10.7 points, while CORE‑EMBED‑8B achieves the best overall average among evaluated embeddings, with gains also transferring to the MCMR benchmark without harming COCO or Flickr30K retrieval.
By Tingyu Song, Mingxin Li, Yanzhao Zhang, Dingkun Long, Chu Liu, Pengjun Xie, Yilun Zhao, Shu Wu
arXiv:2609.10008v1 Announce Type: new
Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gal...
By Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar, Abdelrahman Mohamed Shaker, Rao Anwer
arXiv:2606. 15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched.
By Shubhang Bhatnagar, Dheeraj Baiju, Narendra Ahuja
UMER is a Unified Multimodal Embedding and Ranking framework that combines contrastive embeddings with Pair‑Aware Discriminative Reasoning to improve universal multimodal retrieval. It replaces item‑wise reflection with pair‑wise comparison of query–candidate pairs, enabling explicit identification of matching and discrepancy evidence. A mutual distillation strategy transfers reliable pairwise preferences between the embedding and ranking components, and UMER achieves state‑of‑the‑art performance on the MMEB‑V2 benchmark while supporting budget‑adjustable inference.
By Libiao Chen, Xiyang Liu, Yanheng Wei, Tao Wang, Zhenyu Tang
The paper introduces SRAIN, a framework that learns sample‑wise, rank‑aware interpolation weights for composed visual data retrieval. Instead of relying on complex multimodal large language models, SRAIN uses simple linear interpolation in embedding space, dynamically predicting query‑specific weights through batch‑wise rank‑aware estimation and a compact memory bank for hard negatives. This approach achieves state‑of‑the‑art performance on composed video retrieval and competitive results on composed image retrieval while significantly reducing query‑time latency.
By Boseung Jeong, Taegyu Park, Donghyeon Kwon, Hyunsouk Cho, Suha Kwak
arXiv:2609.12552v1 Announce Type: new
Abstract: Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left t...
By Ma\"elic Neau