Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose VEGAS (Video caption Evaluation via GAze Score), a training-free metric that leverages test-time gaze to sample personalized, attention-aligned text.
arXiv:2607. 08489v1 Announce Type: cross Abstract: Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention.
By Shenghui Chen, Po-han Li, Ximeng Sun, Shijia Yang, Emad Barsoum, Zicheng Liu, Sandeep Chinchali, Ufuk Topcu
arXiv:2604.05475v2 Announce Type: replace
Abstract: Large vision-language models have achieved remarkable capabilities by training on massive internet-scale data, yet a fundamental asymmetry persists...
By Kidus Zewde, Yuchen Zhou, Dennis Ng, Neo Tiangratanakul, Tommy Duong, Ankit Raj, Yuxin Zhang, Xingyu Shen, Simiao Ren
arXiv:2608.07282v2 Announce Type: replace
Abstract: A central question for computational psycholinguistics is to what extent neural language models can serve as cognitively adequate models of human l...
By Rahul Murali Shankar, Titus von der Malsburg, Sebastian Pad\'o
Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, in order to identify the subject of gaze analysis.
arXiv:2608. 11367v1 Announce Type: cross Abstract: Estimating human gaze targets from images in-the-wild is an important and formidable task.
By Xu Cao, Houze Yang, Vipin Gunda, Zhongyi Zhou, Tianyu Xu, Adarsh Kowdle, Inki Kim, James M. Rehg