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cs.CV2026
Teaching Vision-Language Models to Use the Scale They Are Given: Label-Free Equivariance Training for Metric Physical Reasoning
Kaizhen Tan, Yang Feng, Heqing Du +3
Metric questions about video, such as the speed of a moving object, require a vision-language model to convert visual measurements into physical units using a real-world reference…
cs.CV2026
GeoFidelity-Bench: Evaluating Block-Conditioned Geographic Fidelity in Text-to-Image Street-View Generation
Kaizhen Tan, Hanzhe Hong, Siru Tao
Text-to-image models can generate plausible street scenes from prompts such as "a street in New York City," but it remains unclear whether these images resemble the requested place…
cs.CV2026
Confidence under Visual Token Pruning: Removed Evidence and Risk-Controlled Token Budgets for MLLMs
Kaizhen Tan, Yang Feng, Heqing Du +3
Visual token pruning speeds up multimodal large language models (MLLMs) by keeping a small subset of the visual tokens, and pruning methods are compared by the accuracy they retain…