South China Normal University
Proposed DecoupleCSS that addresses catastrophic forgetting and background shift by decoupling class-aware detection from class-agnostic segmentation. Achieved SOTA with 12.12% and 17.99% mIoU improvements.
Proposed EvolveAgent, a trajectory-level iterative optimization framework. Significantly expanded solution space through crossover and mutation, improving baseline from 28% to 58% on SWE-Bench.
First work introducing reinforcement learning into VOS. Combines hierarchical frame sampling and multimodal reasoning with SAM2 and XMem for improved segmentation.
Systematically analyzes existing CISS paradigms and proposes a new modeling method that effectively solves class-incremental semantic segmentation problems.
Addresses sparse rewards and weak generalization in long-horizon tasks through a two-stage framework that evolves trajectories into reusable skills.
Lightweight multimodal 3D segmentation framework with Agent attention mechanism. Reduces inference latency to 20% while maintaining SOTA accuracy on nuScenes and KITTI.