Few-Shot Object Counting with frequency attention and multi-perception head

Abstract

Few-Shot Object Counting (FSC) is a critical technique in computer vision, which focuses on estimating the number of exemplar objects in target tasks. This technique is highly versatile and applicable in diverse domains, such as crowd monitoring, traffic management, and wildlife tracking. The primary challenge in FSC is achieving robust feature matching despite the gap between the diversity of targets and the scarcity of exemplars. In this research, we propose the Few-shot Object Counting Network with Frequency Attention and Multi-Perception Head (FFMP), which aims to enhance the limited examples by identifying additional instances within query images. The FFMP framework comprises three core components: Frequency Domain Feature Fusion (FDF), Self-Adaptive Feature Enhancement (SFE), and Multi-Perception Head (MP). The FDF component fuses features from both spatial and frequency domains to generate more precise similarity maps. The SFE component identifies and focuses on recurrent target features within query images, enriching the initial set of examples and providing a detailed understanding of the target category. Additionally, the MP component integrates counting and detection tasks, thereby improving overall performance. Extensive experiments on the FSC-147 dataset and various class-specific counting datasets demonstrate that FFMP achieves competitive counting performance compared to state-of-the-art methods. Code is available at https://github.com/dsl161/FFMP.