915 citations · 3.2k across the 107 of their papers we have counts for
4 papers · 1 filter
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism
Xin Chen, Hengheng Zhang, Xiaotao Gu +3
The Mixture of Experts (MoE) model becomes an important choice of large language models nowadays because of its scalability with sublinear computational complexity for training and…
Segment Anything in 3D with Radiance Fields
Jiazhong Cen, Jiemin Fang, Zanwei Zhou +5
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results. This paper aims to generalize SAM to segment 3D obj…
Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation
Xinyue Huo, Lingxi Xie, Wengang Zhou +2
We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i…
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Zelin Peng, Guanchun Wang, Lingxi Xie +3
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification net…