Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

2025-05-20

Neural Information Processing Systems

AI Summary

This work addresses classification bias in few-shot object detection (FSOD) and few-shot instance segmentation (FSIS) caused by instance-level label omission—where unlabeled instances in images are erroneously treated as negative examples. To mitigate this, we propose a lightweight dual-head decoupled classifier that splits the standard classification head into two parallel branches: a positive branch for explicitly modeling ground-truth instances, and a noise-negative branch for modeling pseudo-negative instances arising from label omissions. Our approach is the first to formally define and address instance-level label-omission bias. It introduces no additional parameters, computational overhead, or reliance on meta-learning, fine-tuning, or external data; instead, it operates seamlessly within standard two-stage detectors (e.g., Mask R-CNN) under conventional supervised training. Extensive experiments on PASCAL VOC and MS-COCO benchmarks demonstrate consistent and significant improvements over state-of-the-art methods for both FSOD and FSIS. The code is publicly available.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The existing methods severely suffer from bias classification because of the missing label issue which naturally exists in an instance-level few-shot scenario and is first formally proposed by us. Our analysis suggests that the standard classification head of most FSOD or FSIS models needs to be decoupled to mitigate the bias classification. Therefore, we propose an embarrassingly simple but effective method that decouples the standard classifier into two heads. Then, these two individual heads are capable of independently addressing clear positive samples and noisy negative samples which are caused by the missing label. In this way, the model can effectively learn novel classes while mitigating the effects of noisy negative samples. Without bells and whistles, our model without any additional computation cost and parameters consistently outperforms its baseline and state-of-the-art by a large margin on PASCAL VOC and MS-COCO benchmarks for FSOD and FSIS tasks. The Code is available at https://csgaobb.github.io/Projects/DCFS.

Problem

• Addressing bias classification in few-shot object detection and instance segmentation
• Mitigating missing label issues in instance-level few-shot learning
• Decoupling classifier heads to handle clear positives and noisy negatives

Innovation

• Decouples classifier into two heads
• Addresses clear positive and noisy negative samples
• No additional computation cost or parameters