arXiv:2609.14699v1 Announce Type: new Abstract: Visual content shapes audience perception and opinion on social media, and computational social science increasingly relies on automated tools to analy...
arXiv:2608.29590v1 Announce Type: new Abstract: We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely...
The study compares human and vision‑language model (VLM) responses to cross‑modal association tasks, using identical stimuli (a pseudo‑word and two images) and recording both choices and eye movements. While larger VLMs show some alignment with human choices, their attention patterns correlate poorly with human gaze, performing no better than a simple center‑bias baseline. Fine‑tuning VLMs on human choices improves choice alignment but not attention alignment, and training on human gaze improves attention correlation without affecting choice accuracy.
AVAtt is a free‑viewing eye‑tracking dataset comprising Images{} paintings from sixteen art movements and five centuries, with Scanpaths{} scanpaths and Fixations{} fixations collected from Participants{} participants. The dataset provides individual scanpaths, fixation timing, eye‑screen distance, fixation and saliency maps, temporal saliency videos, and rich art metadata. A benchmark of classic and deep saliency models shows significant room for improvement, and 44 scanpath‑similarity measures—including a new language‑free image‑encoder family—are ranked.
arXiv:2608.30210v1 Announce Type: cross Abstract: AI image generators now create face portraits that are hard to tell from real photographs. Vision-language models (VLMs) are increasingly proposed to...
arXiv:2610.07783v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what i...
arXiv:2602. 14834v2 Announce Type: replace-cross Abstract: Human eye movements in visual recognition reflect a balance between foveal sampling and peripheral context.
arXiv:2608. 10195v1 Announce Type: cross Abstract: Human vision organizes what it sees into wholes: same-colored points group into series, similar marks cohere into categories, and shapes complete into recognizable objects.
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.
arXiv:2610.08663v1 Announce Type: new Abstract: Video gaze prediction is led by gaze-trained models, yet gaze-free priors carry signal those models have not absorbed, if one knows when to trust them....
The paper investigates whether adding textual personas to vision‑language models (VLMs) causes the models to perceive images as the described individuals do. Experiments across 14 VLMs show that persona prompts never outperform simple rule‑based baselines and actually erase real differences between groups such as people with low vision, color blindness, or hearing loss. While the models can predict how visual impairments affect humans, the personas do not alter the models’ image representations or answer probabilities, indicating that the models merely state what they know about people rather than seeing like them.
The paper explores using eye tracking to evaluate the visual realism of AI-generated videos, supplementing traditional explicit feedback. Two Visual Turing Test experiments with 90 participants produced 4,286 eye‑tracking trials across 438 videos, revealing that gaze patterns correlate with explicit judgments. The authors introduce GazeEval, a computational model that predicts perceived realism from eye‑tracking data with 71.3% accuracy, and they release the dataset for future research.