PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery

• URL: http://arxiv.org/abs/2407.19001v2
• Date: Mon, 9 Sep 2024 16:43:23 GMT
• Title: PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery
• Authors: Fernando Julio Cendra, Bingchen Zhao, Kai Han,
• Abstract summary: Continual Category Discovery (CCD) aims to automatically discover novel categories in a continuous stream of unlabeled data. We propose PromptCCD, a framework that utilizes a Gaussian Mixture Model (GMM) as a prompting method for CCD. We extend the standard evaluation metric for Generalized Category Discovery (GCD) to CCD and benchmark state-of-the-art methods on diverse public datasets.
• Score: 60.960147451219946
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We tackle the problem of Continual Category Discovery (CCD), which aims to automatically discover novel categories in a continuous stream of unlabeled data while mitigating the challenge of catastrophic forgetting -- an open problem that persists even in conventional, fully supervised continual learning. To address this challenge, we propose PromptCCD, a simple yet effective framework that utilizes a Gaussian Mixture Model (GMM) as a prompting method for CCD. At the core of PromptCCD lies the Gaussian Mixture Prompting (GMP) module, which acts as a dynamic pool that updates over time to facilitate representation learning and prevent forgetting during category discovery. Moreover, GMP enables on-the-fly estimation of category numbers, allowing PromptCCD to discover categories in unlabeled data without prior knowledge of the category numbers. We extend the standard evaluation metric for Generalized Category Discovery (GCD) to CCD and benchmark state-of-the-art methods on diverse public datasets. PromptCCD significantly outperforms existing methods, demonstrating its effectiveness. Project page: https://visual-ai.github.io/promptccd .

Related papers

• Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery [65.16724941038052]
  Category-aware Prototype Generation (CPG) and Discrimi Category 5.3% (DCE) are proposed. CPG enables the model to fully capture the intra-category diversity by representing each category with multiple prototypes. DCE boosts the discrimination ability of hash code with the guidance of the generated category prototypes.
  arXiv  Detail & Related papers  (2024-10-24T23:51:40Z)
• Happy: A Debiased Learning Framework for Continual Generalized Category Discovery [54.54153155039062]
  This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD) C-GCD aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. We introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization.
  arXiv  Detail & Related papers  (2024-10-09T04:18:51Z)
• Contrastive Mean-Shift Learning for Generalized Category Discovery [45.19923199324919]
  We address the problem of generalized category discovery (GCD) We revisit the mean-shift algorithm, i.e., a 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.
  arXiv  Detail & Related papers  (2024-04-15T04:31:24Z)
• Contrastive Continual Multi-view Clustering with Filtered Structural Fusion [57.193645780552565]
  Multi-view clustering thrives in applications where views are collected in advance. It overlooks scenarios where data views are collected sequentially, i.e., real-time data. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. We propose Contrastive Continual Multi-view Clustering with Filtered Structural Fusion.
  arXiv  Detail & Related papers  (2023-09-26T14:18:29Z)
• Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery [36.01459228175808]
  We propose an EM-like framework that alternates between representation learning and class number estimation. We evaluate our framework on both generic image classification datasets and challenging fine-grained object recognition datasets.
  arXiv  Detail & Related papers  (2023-05-10T13:47:38Z)
• Incremental Generalized Category Discovery [26.028970894707204]
  We explore the problem of Incremental Generalized Category Discovery (IGCD) This is a challenging category incremental learning setting where the goal is to develop models that can correctly categorize images from previously seen categories. We present a new method for IGCD which combines non-parametric categorization with efficient image sampling to mitigate catastrophic forgetting.
  arXiv  Detail & Related papers  (2023-04-27T16:27:11Z)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Grow and Merge: A Unified Framework for Continuous Categories Discovery [44.28297337872006]
  We focus on the application scenarios where unlabeled data are continuously fed into the category discovery system. We develop a framework of bf Grow and Merge (bf GM) that works by alternating between a growing phase and a merging phase. In the growing phase, it increases the diversity of features through a continuous self-supervised learning for effective category mining. In the merging phase, it merges the grown model with a static one to ensure satisfying performance for known classes.
  arXiv  Detail & Related papers  (2022-10-09T05:49:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.