# Contrastive Mean-Shift Learning for Generalized Category Discovery

arXiv: https://arxiv.org/abs/2404.09451 · HTML: https://mlsanity.com/paper/2404.09451/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 64 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Learning to Discover Novel Visual Categories via Deep Transfer Clustering](https://mlsanity.com/paper/1908.09884/) (2019)
- [Generalized Category Discovery](https://mlsanity.com/paper/2201.02609/) (2022)
- [Local Aggregation for Unsupervised Learning of Visual Embeddings](https://mlsanity.com/paper/1903.12355/) (2019)
- [Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2102.03526/) (2021)
- [A Unified Objective for Novel Class Discovery](https://mlsanity.com/paper/2108.08536/) (2021)
- [Automatically Discovering and Learning New Visual Categories with Ranking Statistics](https://mlsanity.com/paper/2002.05714/) (2020)
- [Parametric Classification for Generalized Category Discovery: A Baseline Study](https://mlsanity.com/paper/2211.11727/) (2022)
- [Dynamic Conceptional Contrastive Learning for Generalized Category Discovery](https://mlsanity.com/paper/2303.17393/) (2023)
- [PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery](https://mlsanity.com/paper/2212.05590/) (2022)
- [Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery](https://mlsanity.com/paper/2305.06144/) (2023)
- [Mean Shift for Self-Supervised Learning](https://mlsanity.com/paper/2105.07269/) (2021)
- [The Herbarium Challenge 2019 Dataset](https://mlsanity.com/paper/1906.05372/) (2019)
- [Recurrent Pixel Embedding for Instance Grouping](https://mlsanity.com/paper/1712.08273/) (2017)
- [Parametric Information Maximization for Generalized Category Discovery](https://mlsanity.com/paper/2212.00334/) (2022)
- [Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation](https://mlsanity.com/paper/2307.03407/) (2023)
- [Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation](https://mlsanity.com/paper/2111.14353/) (2021)

## Built on by (most cited first, 30 of 39)

- [In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2408.04961/) (2024)
- [SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery](https://mlsanity.com/paper/2408.14371/) (2024)
- [Hyperbolic Category Discovery](https://mlsanity.com/paper/2504.06120/) (2025)
- [Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement](https://mlsanity.com/paper/2507.06928/) (2025)
- [Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction](https://mlsanity.com/paper/2508.10731/) (2025)
- [MOS: Modeling Object-Scene Associations in Generalized Category Discovery](https://mlsanity.com/paper/2503.12035/) (2025)
- [Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery](https://mlsanity.com/paper/2409.19659/) (2024)
- [Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization](https://mlsanity.com/paper/2506.02334/) (2025)
- [Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery](https://mlsanity.com/paper/2507.04725/) (2025)
- [When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach](https://mlsanity.com/paper/2503.14897/) (2025)
- [Category Discovery: An Open-World Perspective](https://mlsanity.com/paper/2509.22542/) (2025)
- [DIG-FACE: De-biased Learning for Generalized Facial Expression Category Discovery](https://mlsanity.com/paper/2409.20098/) (2024)
- [Generalized Category Discovery via Token Manifold Capacity Learning](https://mlsanity.com/paper/2505.14044/) (2025)
- [A Hidden Stumbling Block in Generalized Category Discovery: Distracted Attention](https://mlsanity.com/paper/2507.14315/) (2025)
- [Open Ad-hoc Categorization with Contextualized Feature Learning](https://mlsanity.com/paper/2512.16202/) (2025)
- [AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery](https://mlsanity.com/paper/2410.21705/) (2024)
- [Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts](https://mlsanity.com/paper/2509.26227/) (2025)
- [HIDISC: A Hyperbolic Framework for Domain Generalization with Generalized Category Discovery](https://mlsanity.com/paper/2510.17188/) (2025)
- [Composing Novel Classes: A Concept-Driven Approach to Generalized Category Discovery](https://mlsanity.com/paper/2410.13285/) (2024)
- [Few-Shot Generalized Category Discovery With Retrieval-Guided Decision Boundary Enhancement](https://mlsanity.com/paper/2506.16728/) (2025)
- [FaceGCD: Generalized Face Discovery via Dynamic Prefix Generation](https://mlsanity.com/paper/2507.22353/) (2025)
- [Video-based Generalized Category Discovery via Memory-Guided Consistency-Aware Contrastive Learning](https://mlsanity.com/paper/2509.06306/) (2025)
- [Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery](https://mlsanity.com/paper/2512.12925/) (2025)
- [GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery](https://mlsanity.com/paper/2602.19872/) (2026)
- [TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery](https://mlsanity.com/paper/2603.08075/) (2026)
- [Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery](https://mlsanity.com/paper/2605.08183/) (2026)
- [Component Adaptive Clustering for Generalized Category Discovery](https://mlsanity.com/paper/2507.01711/) (2025)
- [Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels](https://mlsanity.com/paper/2508.21424/) (2025)
- [InfoSculpt: Sculpting the Latent Space for Generalized Category Discovery](https://mlsanity.com/paper/2601.10098/) (2026)
- [SIEFormer: Spectral-Interpretable and -Enhanced Transformer for Generalized Category Discovery](https://mlsanity.com/paper/2602.13067/) (2026)

## Abstract (arXiv)

We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem, we revisit the mean-shift algorithm, i.e., a classic, powerful technique for mode seeking, and incorporate it into a contrastive learning framework. The proposed method, dubbed Contrastive Mean-Shift (CMS) learning, trains an image encoder to produce representations with better clustering properties by an iterative process of mean shift and contrastive update. Experiments demonstrate that our method, both in settings with and without the total number of clusters being known, achieves state-of-the-art performance on six public GCD benchmarks without bells and whistles.