arXiv:2607. 24017v1 Announce Type: cross Abstract: The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws.
By Pengkun Jiao, Bin Zhu, Jingjing Chen, Yu-gang Jiang
OpenVAM is a new framework for visual attention modeling that combines a dense saliency map with language‑based explanations. It uses a decoupled design: a visual pathway for precise localization and a vision‑language head that generates grounded what/why explanations. The method is trained in three stages to preserve localization while adding language grounding, and a scalable pipeline creates multi‑domain annotations for evaluation.
By Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini, Michael Brudno, Babak Taati
arXiv:2609.37581v1 Announce Type: cross
Abstract: Vision-Language Models (VLMs) excel at visual understanding and reasoning but often incur substantial inference costs due to the large number of visu...
By Jing Wang, Zhiping Wu, Dongdong Ren, Youfang Han, Wei Zhao, Wenbin Li
The paper introduces Salience-LLaVA, a vision‑language model that prioritizes scene elements based on their importance for low‑vision users. It presents three new salience‑aware datasets—Salience COCO, Salience Flickr, and Salience VizWiz—annotated with object‑level salience verified by low‑vision participants. The authors also propose the SCMI metric to evaluate caption ordering accuracy and demonstrate the system’s practicality by deploying it on assistive glasses.
By Jiazhao Liang, Hao Huang, Shuaihang Yuan, Congcong Wen, Geeta Chandra Raju Bethala, Giles Hamilton-Fletcher, Yu Hao, John-Ross Rizzo, Mengyu Wang, Anthony Tzes, Yi Fang
arXiv:2609.13706v1 Announce Type: new
Abstract: Co-salient object detection (Co-SOD) requires a model to find foreground regions that are salient in individual images and supported by the image group...
By Yuan Xiang, Matteo Rossi, Yingzhou Chen
Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing training-free alternatives instead ground captions in detected objects to curb hallucination, but apply only a single, fixed correction pass without prioritizing which objects matter most, leaving semantically significant content omitted.