FSAD:Few-Shot Object Detection via Aggregation and Disentanglement

Yunfeng Kou, Kunming Wu, Chenghao Huang, Hu Chen, Wenchao Du, Hong Liu

Abstract: Few-shot Object Detection (FSOD) aims to leverage knowledge gained from general object detectors to enhance future detection tasks for novel object categories. In response to the poor performance observed in commonly used attention-based feature fusion methods, particularly in 1-shot, this paper proposes an enhanced Cross-Attention-Like Aggregation (CAL) Module and a GAN-Disentangled-Like Feature Enhancement (GDL) Module.CAL module utilizes an asymmetric mechanism and neutralized features resulting from concatenation for cross-attention, which improves the model’s generalization ability, enabling it to address issues where the target is entirely unrecognizable in 1-shot. The GDL module captures latent independent information from the support set. It employs a discriminator to stabilize the information extraction process, enabling the attention module to discern relevant information more effectively and accurately guide the query features.Extensive experiments conducted on the PASCAL VOC and COCO datasets demonstrate the superior performance of our approach over strong baselines, showcasing substantial advancements in one-shot and two-shot performance.our code is available at https://github.com/yun1232/FSAD

External IDs:dblp:conf/ijcnn/KouWHCDL24