LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
International journal of computer vision , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DecomPose introduces difficulty-aware gradient decoupling and asymmetric branching to reduce cross-category optimization contention in category-level 6D pose estimation, reporting better results on REAL275, CAMERA25, and HouseCat6D.
citing papers explorer
-
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
-
DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation
DecomPose introduces difficulty-aware gradient decoupling and asymmetric branching to reduce cross-category optimization contention in category-level 6D pose estimation, reporting better results on REAL275, CAMERA25, and HouseCat6D.