TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery

• URL: http://arxiv.org/abs/2603.08075v1
• Date: Mon, 09 Mar 2026 08:14:58 GMT
• Title: TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery
• Abstract summary: On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream.<n>Existing approaches freeze the feature extractor trained offline and employ a hash-based framework that quantizes features into binary codes as class prototypes.<n>We propose a test-time adaptation framework that enables learning through discovery.
• Score: 30.10914068071538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream, using a model trained only on labeled data. Existing approaches freeze the feature extractor trained offline and employ a hash-based framework that quantizes features into binary codes as class prototypes. However, discovering novel categories with a fixed knowledge base is counterintuitive, as the learning potential of incoming data is entirely neglected. In addition, feature quantization introduces information loss, diminishes representational expressiveness, and amplifies intra-class variance. It often results in category explosion, where a single class is fragmented into multiple pseudo-classes. To overcome these limitations, we propose a test-time adaptation framework that enables learning through discovery. It incorporates two complementary strategies: a semantic-aware prototype update and a stable test-time encoder update. The former dynamically refines class prototypes to enhance classification, whereas the latter integrates new information directly into the parameter space. Together, these components allow the model to continuously expand its knowledge base with newly encountered samples. Furthermore, we introduce a margin-aware logit calibration in the offline stage to enlarge inter-class margins and improve intra-class compactness, thereby reserving embedding space for future class discovery. Experiments on standard OCD benchmarks demonstrate that our method substantially outperforms existing hash-based state-of-the-art approaches, yielding notable improvements in novel-class accuracy and effectively mitigating category explosion. The code is publicly available at \textcolor{blue}{https://github.com/ynanwu/TALON}.

Related papers

• Learning through Creation: A Hash-Free Framework for On-the-Fly Category Discovery [35.522047721751626]
  OCD aims to recognize known classes while simultaneously discovering emerging novel categories during inference.<n>It is unreasonable to expect a model optimized on limited labeled data to carry out a qualitatively different discovery objective during inference.<n>We propose Learning through Creation (LTC), a fully feature-based and hash-free framework that injects novel-category awareness directly into offline learning.
  arXiv  Detail & Related papers  (2026-03-14T09:35:31Z)
• Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning [69.28860905525057]
  Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples.<n>We introduce an efficient prototype fine-tuning framework that evolves static centroids into dynamic, learnable components.
  arXiv  Detail & Related papers  (2026-02-05T03:50:53Z)
• MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning [20.22260611239967]
  Few-Shot Class-Incremental Learning (FSCIL) must contend with the dual challenge of learning new classes from scarce samples while preserving old class knowledge.<n>New-class prototypes suffer significant estimation bias due to extreme data scarcity, whereas base-class prototypes benefit from sufficient data.<n>We show that aligning the new-class priors with old-class statistics via Bayesian analysis reduces variance and improves prototype accuracy.
  arXiv  Detail & Related papers  (2025-09-24T00:41:40Z)
• Solving the Catastrophic Forgetting Problem in Generalized Category Discovery [46.63232918739251]
  Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets.<n>Recent state-of-the-art method SimGCD transfers the knowledge from known-class data to the learning of novel classes through debiased learning.<n>We propose a novel learning approach, LegoGCD, which is seamlessly integrated into previous methods to enhance the discrimination of novel classes.
  arXiv  Detail & Related papers  (2025-01-09T14:31:54Z)
• 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.<n>CPG enables the model to fully capture the intra-category diversity by representing each category with multiple prototypes.<n>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)
• Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration [67.69532794049445]
  We find a tendency for existing methods to misclassify the samples of new classes into base classes, which leads to the poor performance of new classes. We propose a simple yet effective Training-frEE calibratioN (TEEN) strategy to enhance the discriminability of new classes.
  arXiv  Detail & Related papers  (2023-12-08T18:24:08Z)
• Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning [68.63910949916209]
  This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. We propose a novel adaptive prototype learning method consisting of two main stages: prototypical representation learning and prototypical self-training. We conduct extensive experiments on four benchmark datasets and demonstrate the effectiveness and robustness of the proposed method with state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-01T16:34:33Z)
• Class-incremental Novel Class Discovery [76.35226130521758]
  We study the new task of class-incremental Novel Class Discovery (class-iNCD) We propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-07-18T13:49:27Z)
• Class-incremental Learning with Pre-allocated Fixed Classifiers [20.74548175713497]
  In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. We propose a novel fixed classifier in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase.
  arXiv  Detail & Related papers  (2020-10-16T22:40:28Z)
• Learning Adaptive Embedding Considering Incremental Class [55.21855842960139]
  Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: 1) Novel class detection. After the novel classes are detected, the model needs to be updated without re-training using entire previous data.
  arXiv  Detail & Related papers  (2020-08-31T04:11:24Z)

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.