Wenbin Li

Task arithmetic works because models internally allocate distinct features to different tasks, and enforcing this specialization via orthogonality regularization unlocks even better editing.

Achieve state-of-the-art performance on ultra-high-resolution remote sensing tasks without the quadratic compute cost, thanks to a query-guided token compression strategy.

MLLMs can aggressively prune visual tokens without sacrificing performance by adapting token reduction strategies to specific classes and prompts.

Forget prompt engineering: this new region proposal network spots objects across diverse datasets without *any* text or image prompts.