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Institute of Computing Technology
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Explicitly grounding evidence in spatial relation tasks can boost VLM performance by nearly 12 points, transforming how we approach visual reasoning.
A staggering 12.73-26.25% of correct decisions in multimodal spatial reasoning are made without proper credit to the supporting images, exposing flaws in current evaluation methods.
Class-Contrastive Influence reveals that the most effective synthetic samples for medical classification are not just realistic but strategically challenging, enhancing model robustness and accuracy.
