At this stage, research in the field of Few-shot image classification (FSC) has made good progress, but there are still many difficulties in the field of Few-shot object detection (FSOD). Almost all of the current FSOD methods face catastrophic forgetting problems, which are manifested in that the accuracy of base class recognition will drop seriously when acquiring the ability to recognize Novel classes. And for many methods, the accuracy of the model will fall back as the class increases. To address this problem we propose a new memory-based method called Memorable Faster R-CNN (MemFRCN), which makes the model remember the categories it has already seen. Specifically, we propose a new tow-stage object detector consisting of a memory-based classifier (MemCla), a fully connected neural network classifier (FCC) and an adaptive fusion block (AdFus). The former stores the embedding vector of each category as memory, which enables the model to have memory capabilities to avoid catastrophic forgetting events. The final part fuses the outputs of FCC and MemCla, which can automatically adjust the fusion method of the model when the number of samples increases so that the model can achieve better performance under various conditions. Our method can perform well on unseen classes while maintaining the detection accuracy of seen classes. Experimental results demonstrate that our method outperforms other current methods on multiple benchmarks.

Convolutional neural network (CNN) based vehicle re-identificatioin (ReID) inevitably has many disadvantages, such as information loss caused by downsampling operation. Therefore we propose a vision transformer (Vit) based vehicle ReID method to solve this problem. To improve the feature representation of vision transformer and make full use of additional vehicle information, the following methods are presented. (I) We propose a Quadratic Split Architecture (QSA) to learn both global and local features. More precisely, we split an image into many patches as “global part” and further split them into smaller sub-patches as “local part”. Features of both global and local part will be aggregated to enhance the representation ability. (II) The Auxiliary Information Embedding (AIE) is proposed to improve the robustness of the model by plugging a learnable camera/viewpoint embedding into Vit. Experimental results on several benchmarks indicate that our method is superior to many advanced vehicle ReID methods.