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Treating robot skill calls as runtime-verified execution proposals rather than blind action predictions enables self-correcting VLA autonomy that reaches 97.4% success on LIBERO.
Segment-level explainable forensics can drastically enhance our ability to detect and interpret localized manipulations in lengthy AI-generated videos.
InstructSAM equips SAM with high-level instruction understanding and compositional reasoning for multi-instance segmentation, all without modifying SAM's core architecture.
Object-centric vision could be the key to unlocking LMMs' potential for precise object manipulation and fine-grained spatial reasoning, capabilities currently beyond their reach.
VLMs can now drive embodied agents to navigate complex environments with unprecedented efficiency, thanks to a novel framework that bridges the gap between 2D semantic understanding and 3D spatial reasoning.
