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cs.CV2025
SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation
Wenyu Zhang, Wei En Ng, Lixin Ma +5
Current vision-language models may grasp basic spatial cues and simple directions (e.g. left, right, front, back), but struggle with the multi-dimensional spatial reasoning necessa…
cs.CV2024
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2
Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…
cs.CV2024
What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases
Anthony Meng Huat Tiong, Junqi Zhao, Boyang Li +3
Vision-language (VL) models, pretrained on colossal image-text datasets, have attained broad VL competence that is difficult to evaluate. A common belief is that a small number of…