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

TALON is a test-time adaptation framework for on-the-fly category discovery (OCD) that enables a model to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream.

This repository contains the official implementation and weights for the paper TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery, presented at CVPR 2026.

• GitHub: https://github.com/ynanwu/TALON
• Paper: arXiv:2603.08075

Method Overview

Existing OCD methods often freeze the feature extractor, which limits the learning potential of incoming data. TALON addresses this with two complementary strategies:

1. Semantic-aware prototype update: Dynamically refines class prototypes to improve classification.
2. Stable test-time encoder update: Integrates new information directly into the parameter space.
3. Margin-aware logit calibration: Applied during the offline stage to reserve embedding space for future class discovery.

Installation

This project uses uv for dependency management.

# Clone the repository
git clone https://github.com/ynanwu/TALON
cd TALON

# Install all dependencies
uv sync

Usage

To evaluate a pretrained checkpoint (e.g., CUB with a CLIP backbone):

uv run test.py --dataset_name cub --backbone clip --ckpt_path checkpoints/clip/cub/best_model.pth

Citation

If you find this work useful for your research, please consider citing the paper:

@inproceedings{talon2026,
  title={TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery},
  author={Wu, Yanan and Yan, Yuhan and Chen, Tailai and Chi, Zhixiang and Wu, ZiZhang and Jin, Yi and Wang, Yang and Li Zhenbo},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}