DORI benchmark shows top vision-language models reach only 54.2% accuracy on coarse orientation tasks and 33% on granular judgments, with sharp drops on reference-frame shifts and compound rotations.
In: Proceedings of the Computer Vision and Pattern Recognition Conference
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AtlasVA organizes VLM agent memory into spatial heatmaps, visual exemplars, and symbolic skills, evolving atlases from trajectories to act as potential-based shaping rewards in teacher-free reinforcement learning.
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Seeing Isn't Orienting: A Cognitively Grounded Benchmark Reveals Systematic Orientation Failures in MLLMs
DORI benchmark shows top vision-language models reach only 54.2% accuracy on coarse orientation tasks and 33% on granular judgments, with sharp drops on reference-frame shifts and compound rotations.
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AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents
AtlasVA organizes VLM agent memory into spatial heatmaps, visual exemplars, and symbolic skills, evolving atlases from trajectories to act as potential-based shaping rewards in teacher-free reinforcement learning.