arXiv Computer Vision
Human versus Computer Vision
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arXiv Computer Vision
Sep 15
Perceive, Refine, Reason: A Calibrated Pipeline for Measuring Indicators in Strategic Visual Communication on Social Media
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 Computer Vision
Sep 1
Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models
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...
arXiv AI
Sep 30
Similar Choices, Different Attention: Cross-Modal Associations in Humans and Vision-Language Models
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.
arXiv Computer Vision
Oct 23
AVAtt: A Free-Viewing Eye-Tracking Dataset and Benchmark for Visual Attention on Paintings
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 Computer Vision
Sep 1
Frontier vision-language models have overtaken young adults at detecting AI-generated portraits -- but not their calibration
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 Computer Vision
Oct 7
From Laboratory to Road: Evaluating Wearable Gaze Accuracy for Driving
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...