@inproceedings{
gao2024psttl,
title={{PS}-{TTL}: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection},
author={Yingjie Gao and Yanan Zhang and Ziyue Huang and Nanqing Liu and Di Huang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=APhxP6VCYs}
}
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to learn models with strong generalization power using extremely limited annotated data. Although the fine-tuning-based paradigm for FSOD has become mainstream, where detectors are initially pretrained on base classes with sufficient samples and then fine-tuned on novel classes with few annotated samples, the scarcity of samples in novel classes hampers the precise capture of their data distribution. To address this issue, we propose a novel framework for few-shot object detection, namely Prototype-based Soft-labels and Test-Time Learning (PS-TTL). Specifically, we design a Test-Time Learning (TTL) module that employs a mean-teacher network for self-training to discover novel instances on test data, effectively alleviating the problem of overfitting to the distribution of base class. Furthermore, we develop a Prototype-based Soft-labels (PS) strategy via assessing similarities between pseudo-labels and category prototypes to unleash the potential of low-quality pseudo-labels, thereby significantly mitigating the constraints posed by few-shot samples. Extensive experiments on both the VOC and COCO benchmarks show that PS-TTL achieves a new state-of-the-art, highlighting its effectiveness.
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
Yingjie Gao Yanan Zhang Ziyue Huang Nanqing Liu Di Huang