Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference

Abstract

Aiming at recognizing and localizing the object of novel categories by a few reference samples, few-shot object detection is a quite challenging task. Previous works often depend on the fine-tuning process to transfer their model to the novel category and rarely consider the defect of fine-tuning, resulting in many drawbacks. For example, these methods are far from satisfying in the low-shot or episode-based scenarios since the finetuning process in object detection requires much time and highshot support data. To this end, this paper proposes a plug-andplay few-shot object detection (PnP-FSOD) framework that can accurately and directly detect the objects of novel categories without the fine-tuning process. To accomplish the objective, the PnP-FSOD framework contains two parallel techniques to address the core challenges in the few-shot learning, i.e., across-category task and few-annotation support. Concretely, we first propose two simple but effective meta strategies for the box classifier and RPN module to enable the across-category object detection without fine-tuning. Then, we introduce two explicit inferences into the localization process to reduce its dependence on the annotated data, including explicit localization score and semi-explicit box regression. In addition to the PnP-FSOD framework, we propose a novel one-step tuning method that can avoid the defects in fine-tuning. It is noteworthy that the proposed techniques and tuning method are based on the general object detector without other prior methods, so they are easily compatible with the existing FSOD methods. Extensive experiments show that the PnP-FSOD framework has achieved the state-of-the-art fewshot object detection performance without any tuning method. After applying the one-step tuning method, it further shows a significant lead in both efficiency, precision, and recall, under varied few-shot evaluation protocols.

DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection

“…To generate high-quality proposals and make them discriminative, Zhang et al [37] used support query mutual guidance and hybrid loss. In terms of enhancing the classifier and regressor, Li et al [38] added a correction network in the support branch to refine the classification scores, and Huang et al [39] proposed a dynamic classifier and semi-explicit regressor to improve the generalizability. Most networks are based on Faster R-CNN [3]; specifically, the methods in [40,41] were improved based on DETR [42] and VIT [43].…”