2 citations · 2 across the 5 of their papers we have counts for
5 papers
Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic Segmentation
Hongwei Niu, Linhuang Xie, Jianghang Lin +1
Domain Generalized Semantic Segmentation (DGSS) seeks to utilize source domain data exclusively to enhance the generalization of semantic segmentation across unknown target domains…
EOV-Seg: Efficient Open-Vocabulary Panoptic Segmentation
Hongwei Niu, Jie Hu, Jianghang Lin +2
Open-vocabulary panoptic segmentation aims to segment and classify everything in diverse scenes across an unbounded vocabulary. Existing methods typically employ two-stage or singl…
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
Liujuan Cao, Jianghang Lin, Zebo Hong +4
Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum. In…
Depth-Guided Semi-Supervised Instance Segmentation
Xin Chen, Jie Hu, Xiawu Zheng +3
Semi-Supervised Instance Segmentation (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled i…
Active Teacher for Semi-Supervised Object Detection
Peng Mi, Jianghang Lin, Yiyi Zhou +7
In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{…