Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
This work addresses the challenge of detecting objects with limited training data, which is crucial for applications like robotics and surveillance, but it is incremental as it builds on existing meta-learning methods.
The paper tackles the problem of few-shot object detection by proposing a hierarchical attention network and meta-contrastive learning to better exploit features and improve classification, achieving state-of-the-art results with AP improvements of 2.3% to 3.4% on the COCO dataset.
Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and query images owing to structural limitations. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. In addition, meta-learning does not distinguish the categories well because it determines whether the support and query images match. In other words, metric-based learning for classification is ineffective because it does not work directly. Thus, we propose a contrastive learning method called meta-contrastive learning, which directly helps achieve the purpose of the meta-learning strategy. Finally, we establish a new state-of-the-art network, by realizing significant margins. Our method brings 2.3, 1.0, 1.3, 3.4 and 2.4% AP improvements for 1-30 shots object detection on COCO dataset. Our code is available at: https://github.com/infinity7428/hANMCL