"prototype learning" Papers
13 papers found
Conference
3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation
Gyeongrok Oh, Sung June Kim, Heeju Ko et al.
CVPR 2025arXiv:2503.15185
6
citations
Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation
Tianyu Zou, Shengwu Xiong, Ruilin Yao et al.
ICCV 2025arXiv:2507.19140
1
citations
Differentially Private Prototypes for Imbalanced Transfer Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic et al.
AAAI 2025paperarXiv:2406.08039
2
citations
GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models Through Statistically-Guided Geo-Prototyping
Bang An, Xun Zhou, Zirui Zhou et al.
AAAI 2025paperarXiv:2412.15353
1
citations
High-dimension Prototype is a Better Incremental Object Detection Learner
Yanjie Wang, Liqun Chen, Tianming Zhao et al.
ICLR 2025
Interpretable Image Classification via Non-parametric Part Prototype Learning
Zhijie Zhu, Lei Fan, Maurice Pagnucco et al.
CVPR 2025arXiv:2503.10247
8
citations
Diffusion Models for Open-Vocabulary Segmentation
Laurynas Karazija, Iro Laina, Andrea Vedaldi et al.
ECCV 2024arXiv:2306.09316
60
citations
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation
Tianpei Zou, Sanqing Qu, Zhijun Li et al.
ECCV 2024arXiv:2407.12387
7
citations
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
Aaron Li, Robin Netzorg, Zhihan Cheng et al.
ICML 2024arXiv:2307.03887
4
citations
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann et al.
AAAI 2024paperarXiv:2312.09783
8
citations
Prototypical Transformer As Unified Motion Learners
Cheng Han, Yawen Lu, Guohao Sun et al.
ICML 2024arXiv:2406.01559
9
citations
Pseudo-Embedding for Generalized Few-Shot Point Cloud Segmentation
Chih-Jung Tsai, Hwann-Tzong Chen, Tyng-Luh Liu
ECCV 2024
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport
Bin Li, Ye Shi, Qian Yu et al.
AAAI 2024paperarXiv:2402.18411
14
citations