Generalized Category Discovery

Follow this topic Ask about this topic

Generalized category discovery studies how to recognize labeled categories while discovering new ones in an unlabeled mixture of familiar and unseen examples. Common approaches adapt pretrained vision or vision-language models with contrastive learning, prototypes, pseudo-labels, and relational or semantic-concept structure; newer work also uses compositional visual parts and frequency cues. Research extends these methods to continual and online learning, domain shifts, multimodal data, graphs, and medical imaging, with evaluation often measuring accuracy on both known and novel classes. A central tension is separating genuinely new categories from difficult known examples while preserving established knowledge, especially as data distributions shift or evidence arrives over time.

Latest papers

25 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Test-Time Generalized Category Discovery
Sep 27, 2026Shambhavi Mishra, Omprakash Chakraborty, Julio Silva-Rodriguez +3Test-Time Adaptation of VLMs Generalized Category Discovery
ÉTS Montréal, Canada
Generalized Biomedicine Discovery
Sep 9, 2026Luyao Tang, Yingkai Yang, Hanqi Chen +3Generalized Category Discovery Medical Image Anomaly Detection
The University of Hong Kong, Hong Kong SAR, China · Shenzhen University, Shenzhen, China · Xiamen University, Xiamen, China
CoGe-GCD: Reframing Generalized Category Discovery with Compositional Generalization
Sep 9, 2026Luyao Tang, Jiewei Zheng, Kunze Huang +3Geometric Representation Learning Classification
The University of Hong Kong, Hong Kong SAR, China · Xiamen University, Xiamen, China · Shenzhen University, Shenzhen, China.
CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery
Aug 26, 2026Yuanpei Liu, Zhenqi He, Jialu Tang +1Generalized Category Discovery Relational Knowledge Distillation
Visual AI Lab, The University of Hong Kong, Hong Kong SAR
Fourier Self-Supervision for Fine-Grained Generalized Category Discovery
Aug 9, 2026Sarah Rastegar, Mina Ghadimi Atigh, Pascal Mettes +2Frequency-Domain Feature Learning Fine-Grained Image Classification
University of Amsterdam · University of Technology Nuremberg
MedXplore: Towards Reliable and Unbiased Generalized Category Discovery in Medical Imaging
Jul 30, 2026Jianwei He, Kailin Lyu, Junhao Dong +6Medical Image Representation Learning Generalized Category Discovery
Institute of Automation, Chinese Academy of Sciences Beijing, China · Nanyang Technological University Singapore, Singapore
DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery
Jul 15, 2026Peiyan Gu, Zixin Teng, Xuming HeDirichlet Process Mixture Models Bayesian Mixture Models
ShanghaiTech University, Shanghai, China · Shanghai Engineering Research Center of Intelligent Vision and Imaging
Virtual Category-Guided Continual Generalized Category Discovery
Jul 6, 2026Jiahui Xiong, Qiuxia Lai, Hongsong WangGeneralized Category Discovery
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China · State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
Explainable Novel Category Discovery in Semantic Concept Space
Jul 5, 2026Ifrat Ikhtear Uddin, Yang Zhou, KC Santosh +1Concept Bottleneck Models Concept-Based Explanations
Department of Computer Science, University of South Dakota, USA · Department of Computer Science and Software Engineering, Auburn University, USA
Identifying Latent Concepts and Structures for Generalized Category Discovery
Jul 1, 2026Boyang Dai, Chaoqi Chen, Yizhou YuRepresentation Learning Visual Representation Learning
1The University of Hong Kong · 2Shenzhen University
Synergistic Dual-Branch Adaptation for Multi-modal Generalized Category Discovery
Jun 19, 2026Yuxun Qu, Minyu Zhou, Yongqiang Tang +2Cross-Modal Alignment Multimodal Classification
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 101408, China
Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discovery
May 10, 2026Yulin Xu, Chunqi Guo, Yuanzhen Shuai +1Semi-Supervised Learning Generalized Category Discovery
University College London London, UK · Juniata College Huntingdon, USA
Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery
May 5, 2026Bo Ye, Kai Gan, Tong Wei +1Vision Foundation Model Adaptation Parameter-Efficient Fine-Tuning
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China, and the Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China.
Generalized Category Discovery in Federated Graph Learning
May 5, 2026Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +2Federated Graph Learning Federated Learning
Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China
Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models
Apr 29, 2026Hongjun Wang, Po Hu, Kai HanVLM Adaptation Vision Foundation Model Adaptation
School of Computing and Data Science, The University of Hong Kong
Unlocking Optical Prior: Spectrum-Guided Knowledge Transfer for SAR Generalized Category Discovery
Apr 24, 2026Jingyuan Xia, Ruikang Hu, Ye Li +3Domain Adaptation Frequency-Domain Feature Learning
College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China · State Key Laboratory of Complex System Simulation and Modeling Technology, Beijing, 100091, China
OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism
Apr 16, 2026Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh +2Generalized Category Discovery
SAIVT, QUT, Australia · Shield AI, Australia · La Trobe University, Australia
PACO: Proxy-Task Alignment and Online Calibration for On-the-Fly Category Discovery
Apr 13, 2026Weidong Tang, Bohan Zhang, Zhixiang Chi +3Generalized Category Discovery Model Calibration
China Agricultural University, College of Information and Electrical Engineering, Beijing, China · Concordia University, Department of Computer Science and Software Engineering, Montreal, Canada
The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery
Mar 25, 2026Haiyang Zheng, Nan Pu, Yaqi Cai +4Generalized Category Discovery Gradient Interference
University of Trento, Italy · Hefei University of Technology, China
Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery
Mar 20, 2026Jizhou Han, Chenhao Ding, Yuhang He +4Generalized Category Discovery
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · School of Software Engineering, Xi’an Jiaotong University · Harbin Institute of Technology, Shenzhen +1
FOCUS: Bridging Fine-Grained Recognition and Open-World Discovery across Domains
Mar 15, 2026Vaibhav Rathore, Divyam Gupta, Moloud Abdar +2Generalized Category Discovery Domain Generalization
Indian Institute of Technology Bombay, Mumbai, India. · Northeastern University, Boston, USA. · The University of Queensland, Brisbane, Australia.
Learning through Creation: A Hash-Free Framework for On-the-Fly Category Discovery
Mar 14, 2026Bohan Zhang, Weidong Tang, Zhixiang Chi +4Generalized Category Discovery
College of Information and Electrical Engineering, China Agricultural University, China · Department of Electrical and Computer Engineering, University of Toronto, Canada · School of Computer and Information Technology, Beijing Jiaotong University, China +1
Multi-Modal Representation Learning via Semi-Supervised Rate Reduction for Generalized Category Discovery
Feb 23, 2026Wei He, Xianghan Meng, Zhiyuan Huang +3Generalized Category Discovery Cross-Modal Representation Learning
School of Artificial Intelligence, Beijing University of Posts and Telecommunications · Intellifusion Inc., Shenzhen, P.R. China
GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery
Feb 23, 2026Jizhou Han, Chenhao Ding, SongLin Dong +4Generalized Category Discovery Latent Space Alignment
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · School of Software Engineering, Xi’an Jiaotong University · Shenzhen University of Advanced Technology +1
SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery
Feb 19, 2026Lorenzo Caselli, Marco Mistretta, Simone Magistri +1Cross-Modal Learning Cross-Modal Representation Learning
University of Florence, Media Integration and Communication Center (MICC), Italy

Sep 27, 2026J/K move · Enter open · S save

Test-Time Generalized Category Discovery

Shambhavi Mishra, Omprakash Chakraborty, Julio Silva-Rodriguez, Ismail Ben Ayed+2

ÉTS Montréal, Canada

Test-Time Adaptation of VLMs Generalized Category Discovery

Open paper AskPDF arXiv

Test-Time Adaptation (TTA) and Generalized Category Discovery (GCD) are traditionally treated as disjoint problems: the former adapts models to domain shift assuming all test classes are known, while the latter discovers novel categories assuming labeled training data for known classes. However, real-world deployment rarely fits either setting. Motivated by this gap, we introduce Test-Time Generalized Category Discovery (TT-GCD), a unified and more realistic scenario where a vision-language model must adapt to distribution shifts, classify known categories using only textual supervision, and discover novel categories, all during test time and without access to labeled data. To address this challenging scenario, we propose PACT (Prototype Assignment for Category discovery at Test time), a fully unsupervised framework that casts known-class recognition and novel-class discovery via prototype assignment. PACT first re-aligns shifted visual features with the text-derived class representations of the VLM using confident zero-shot predictions. Known and novel categories are then both represented by prototypes in the visual embedding space, estimated from the unlabeled test stream, and each test image is assigned to the category whose prototype is most similar to its visual feature. Extensive experiments across corruption and domain-shift benchmarks demonstrate that PACT outperforms adapted state-of-the-art TTA and GCD methods, effectively bridging the gap between adaptation and discovery.