1 citations · 2 across the 5 of their papers we have counts for
5 papers
MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control
Yuchi Wang, Dingkang Yang, Haiyang Yu +5
MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly incorporating chain-of-thought reason…
Beyond Superficial Unlearning: Sharpness-Aware Robust Erasure of Hallucinations in Multimodal LLMs
Xianya Fang, Feiyang Ren, Xiang Chen +4
Multimodal LLMs are powerful but prone to object hallucinations, which describe non-existent entities and harm reliability. While recent unlearning methods attempt to mitigate this…
SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model
Lin Lin, Jiefeng Long, Zhihe Wan +15
Multimodal embedding models aim to yield informative unified representations that empower diverse cross-modal tasks. Despite promising developments in the evolution from CLIP-based…
SAIL-VL2 Technical Report
Weijie Yin, Yongjie Ye, Fangxun Shu +11
We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves…
Recurrent Alignment with Hard Attention for Hierarchical Text Rating
Chenxi Lin, Jiayu Ren, Guoxiu He +3
While large language models (LLMs) excel at understanding and generating plain text, they are not tailored to handle hierarchical text structures or directly predict task-specific…