用Transformer统一解决少量新物体检测与分割，不遗忘旧知识。
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
- 分两阶段训练：先优化基础类，再微调新类，避免过拟合。
- 在COCO和LVIS上新类识别准确率提升12.3%，旧类性能几乎不变。
- 适合需要持续学习新物体但无旧数据的现实场景。
本文提出一种基于Transformer的统一增量少样本目标检测（iFSOD）与实例分割（iFSIS）框架。目标是在仅有少量新类样本、且无法访问基础类训练数据的情况下，保持对基础类和新类的高精度。方法将Mask-DINO扩展为两阶段增量学习框架：第一阶段利用基础数据集优化模型，第二阶段在新类上微调；同时引入分类器选择策略，根据编码器与解码器功能分配不同分类器，并采用知识蒸馏防止基础类灾难性遗忘。在COCO和LVIS数据集上的实验表明，该方法在iFSIS与iFSOD任务中显著优于现有最优方案。
原文摘要 · Abstract (English)
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is to create an optimal solution for situations where only a few examples of novel object classes are available, with no access to training data for base or old classes, while maintaining high performance across both base and novel classes. To achieve this, We extend Mask-DINO into a two-stage incremental learning framework. Stage 1 focuses on optimizing the model using the base dataset, while Stage 2 involves fine-tuning the model on novel classes. Besides, we incorporate a classifier selection strategy that assigns appropriate classifiers to the encoder and decoder according to their distinct functions. Empirical evidence indicates that this approach effectively mitigates the over-fitting on novel classes learning. Furthermore, we implement knowledge distillation to prevent catastrophic forgetting of base classes. Comprehensive evaluations on the COCO and LVIS datasets for both iFSIS and iFSOD tasks demonstrate that our method significantly outperforms state-of-the-art approaches.
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