6 papers · 1 filter
DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery
Peiyan Gu, Zixin Teng, Xuming He
On-the-fly category discovery requires deciding for each incoming test sample whether to assign it to an existing category or spawn a new one. Existing methods typically implement…
Freeze and Cluster: A Simple Baseline for Rehearsal-Free Continual Category Discovery
Chuyu Zhang, Xueyang Yu, Peiyan Gu +1
This paper addresses the problem of Rehearsal-Free Continual Category Discovery (RF-CCD), which focuses on continuously identifying novel class by leveraging knowledge from labeled…
Composing Novel Classes: A Concept-Driven Approach to Generalized Category Discovery
Chuyu Zhang, Peiyan Gu, Xueyang Yu +1
We tackle the generalized category discovery (GCD) problem, which aims to discover novel classes in unlabeled datasets by leveraging the knowledge of known classes. Previous works…
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation
Ruijie Xu, Chuyu Zhang, Hui Ren +1
We tackle the novel class discovery in point cloud segmentation, which discovers novel classes based on the semantic knowledge of seen classes. Existing work proposes an online poi…
SPOT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering
Chuyu Zhang, Hui Ren, Xuming He
Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant pro…
POT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering
Chuyu Zhang, Hui Ren, Xuming He
Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant pro…