OpenSAL360 is an open‑source platform that enables scalable, low‑cost collection of 360° video saliency data using only a standard screen, mouse, and internet connection. It bypasses the need for VR headsets, allowing parallel data collection from crowdsourced assessors. The authors validated the protocol against seven VR eye‑tracking datasets, performed ablation studies, and released a new dataset of 500 omnidirectional videos annotated by over 2,000 assessors, the largest in the field to date.
By Alexey Bryncev, Andrey Moskalenko, Kira Shilovskaya, Ivan Kosmynin, Dmitriy Vatolin
arXiv:2608. 08947v1 Announce Type: cross Abstract: Current hazard detection systems in autonomous driving may develop mesa objectives, learned internal goals that achieve high training performance through spurious correlations rather than genuine hazard recognition.
By Lennox Anderson, Ahmed Boutar, Jonah Mulcrone, Tal Erez
EgoHRV is a method that estimates heart rate variability (HRV) and heart rate (HR) from the gaze cameras in egocentric headsets. It uses a 3D backbone and a low–high decomposition module to extract the blood volume pulse signal from gaze video, and aligns frequency‑domain representations of contact‑based and camera‑derived signals through cross‑domain pretraining. The approach achieves state‑of‑the‑art accuracy for HR and HRV estimation and, when integrated into EgoExo4D’s proficiency estimator, improves accuracy by 17.8%.
TRACE (Temporal Audit and Condition-aware Evaluation) is a new benchmark and evaluation framework for streaming video understanding that explicitly records when evidence becomes valid, how visual history is maintained, and how responses are triggered. It combines temporally audited visual tasks, evidence timing, instruction-dependent trigger annotations, a unified causal Core–Adapter protocol, and multidimensional reporting of answer quality, timeliness, response-selection behavior, workload, completion, and reliability. Using 1,240 records from 517 videos, TRACE evaluated eight publicly available models in eight configurations, revealing that similar QA accuracy can hide significant differences in completion, answer validity, generation workload, and proactive performance metrics such as response delay, false alarms, and missed target windows.
By Yibo Ma, Qianqian Zhang, Peng Liu, Tiancheng Zhao
arXiv:2610.00922v1 Announce Type: new
Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict...
By Jungmin Lee, Niamat Ullah, Yoseob Han
arXiv:2610.00960v1 Announce Type: new
Abstract: A video benchmark should reward the capability it claims to measure, yet models can exploit answer options, question text, or partial visual evidence....
By Enxin Song, Yinuo Xu, Shusheng Yang, Wenhao Chai, Jiatao Gu