6 papers
Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval
Runhao Li, Xiaoxu Ma, Zhenyu Weng +5
Unsupervised cross-modal hashing enables efficient retrieval of semantically related instances across different modalities without requiring manual semantic annotation. However, ex…
Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
Runhao Li, Xiaoxu Ma, Zhenyu Weng +5
Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existi…
HOI-aware Adaptive Network for Weakly-supervised Action Segmentation
Runzhong Zhang, Suchen Wang, Yueqi Duan +3
In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed network to predict the action of…
PointVDP: Learning View-Dependent Projection by Fireworks Rays for 3D Point Cloud Segmentation
Yang Chen, Yueqi Duan, Haowen Sun +3
In this paper, we propose view-dependent projection (VDP) to facilitate point cloud segmentation, designing efficient 3D-to-2D mapping that dynamically adapts to the spatial geomet…
Ambiguity-aware Point Cloud Segmentation by Adaptive Margin Contrastive Learning
Yang Chen, Yueqi Duan, Haowen Sun +2
This paper proposes an adaptive margin contrastive learning method for 3D semantic segmentation on point clouds. Most existing methods use equally penalized objectives, which ignor…
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation
Yang Chen, Yueqi Duan, Runzhong Zhang +1
In this paper, we propose an adaptive margin contrastive learning method for 3D point cloud semantic segmentation, namely AMContrast3D. Most existing methods use equally penalized…