29 citations · 70 across the 41 of their papers we have counts for
7 papers · 1 filter
Towards Few-shot Out-of-Distribution Detection
Jiuqing Dong, Yongbin Gao, Heng Zhou +4
Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems. Despite the notable advancements in existing OOD detection methodolo…
CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic Segmentation
Jun Cen, Shiwei Zhang, Yixuan Pei +5
2D RGB images and 3D LIDAR point clouds provide complementary knowledge for the perception system of autonomous vehicles. Several 2D and 3D fusion methods have been explored for th…
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Youquan Liu, Lingdong Kong, Jun Cen +5
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a novel fram…
SAD: Segment Any RGBD
Jun Cen, Yizheng Wu, Kewei Wang +6
The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while…
Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition
Jun Cen, Shiwei Zhang, Xiang Wang +4
Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertai…
CLIP-guided Prototype Modulating for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Jun Cen +4
Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explore…
The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
Jun Cen, Di Luan, Shiwei Zhang +5
Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to…