arXiv Computer Vision
Vision Models Predict Urban Scene Appraisal with Limited Neural Alignment
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Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?
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Human-like Object Grouping in Self-supervised Vision Transformers
arXiv:2603. 13994v2 Announce Type: replace-cross Abstract: Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties.
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What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?
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Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
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Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations
arXiv:2606. 24716v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence.
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MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos
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