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University College London
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A simple training framework boosts pixel-level tampering detection performance in VLMs by over 26%, showcasing the power of balanced sampling and late-injection strategies.
EgoPressureDiff not only outperforms traditional methods but also effectively resolves visual-physical ambiguities in grasp pressure estimation for complex 3D interactions.
CLIP models, despite their prowess, stumble when understanding 360掳 images, failing to maintain semantic alignment under horizontal circular shifts.
