arXiv Computer Vision
Vision Models Predict Urban Scene Appraisal with Limited Neural Alignment
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arXiv Computer Vision
Sep 23
Do Vision Model See Like the Brain? A Comparison Across EEG Encoding Model
arXiv:2609.26512v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) and vision transformers are both used to model the human visual system, but whether the two architectures diverge...
arXiv Computer Vision
Oct 15
Capability $\neq$ Interpretability: Human Interpretability of Vision Foundation Models
arXiv:2605.20337v2 Announce Type: replace Abstract: How interpretable are the features of leading vision models? The question is increasingly pressing as these models move from research benchmarks in...
arXiv Machine Learning
Jun 19
Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?
arXiv:2605. 20448v2 Announce Type: replace-cross Abstract: Vision-language models reliably name objects in a scene, but do they represent the 3D layout those objects inhabit?
arXiv Computer Vision
Sep 16
Extremely coarse learning objectives induce human-aligned representations in AI vision models
arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that...
arXiv AI
Jul 7
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.
arXiv Computer Vision
Oct 2
Beyond Localization: A Comprehensive Benchmark of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
The paper introduces PCSR-Bench, a benchmark of 84,373 question‑answer pairs derived from 2,600 omnidirectional images across 26 indoor environments, designed to evaluate perspective‑conditioned spatial reasoning (PCSR) in multimodal large language models (MLLMs). It reports a significant perception–reasoning gap, with accuracy dropping from 57.59% on limited field‑of‑view reasoning to as low as 0.64% on open‑ended compositional directional chains. An RL‑based diagnostic study on a 7B‑scale model shows that reward shaping can improve performance to 60.06% on a controlled task, indicating partial plasticity of PCSR capabilities.