1 citations · 1 across the 4 of their papers we have counts for
5 papers
Semantic Saliency from Multi-Modal Large Language Model Scene Understanding Maps
Ziqi Wen, Shravan Murlidaran, Jonathan Skaza +2
Image-computable low-level image saliency has shaped perceptual psychology and computer science, but is limited by its inability to capture human high-level cognitive factors that…
Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models
Shravan Murlidaran, Miguel P. Eckstein
Vision language models (VLMs) have made remarkable progress in visual reasoning during the last decade. Most evaluations have used simple scenes (MS-COCO) that do not showcase comp…
Why We Look Where We Look: Emergent Human-like Fixations of a Foveated Visual Language Model Maximizing Scene Understanding
Shravan Murlidaran, Ziqi Wen, Sana Shehabi +1
When humans view scenes without a specific task (free-viewing), they initially direct their eye movements toward the scene center and then fixate on people, text, objects being gaz…
Predicting Reaction Time to Comprehend Scenes with Foveated Scene Understanding Maps
Ziqi Wen, Jonathan Skaza, Shravan Murlidaran +2
Although models exist that predict human response times (RTs) in tasks such as target search and visual discrimination, the development of image-computable predictors for scene und…
Comparing Visual Reasoning in Humans and AI
Shravan Murlidaran, William Yang Wang, Miguel P. Eckstein
Recent advances in natural language processing and computer vision have led to AI models that interpret simple scenes at human levels. Yet, we do not have a complete understanding…