Spatial Reasoning for Few-Shot Object Detection
This work addresses the challenge of few-shot object detection for computer vision applications, offering a novel approach that leverages contextual information to reduce data requirements.
The paper tackles the problem of detecting novel objects with few training examples by proposing a spatial reasoning framework that uses geometric relationships between objects to enhance feature representation, achieving significant performance improvements over state-of-the-art methods on PASCAL VOC and MS COCO datasets.
Although modern object detectors rely heavily on a significant amount of training data, humans can easily detect novel objects using a few training examples. The mechanism of the human visual system is to interpret spatial relationships among various objects and this process enables us to exploit contextual information by considering the co-occurrence of objects. Thus, we propose a spatial reasoning framework that detects novel objects with only a few training examples in a context. We infer geometric relatedness between novel and base RoIs (Region-of-Interests) to enhance the feature representation of novel categories using an object detector well trained on base categories. We employ a graph convolutional network as the RoIs and their relatedness are defined as nodes and edges, respectively. Furthermore, we present spatial data augmentation to overcome the few-shot environment where all objects and bounding boxes in an image are resized randomly. Using the PASCAL VOC and MS COCO datasets, we demonstrate that the proposed method significantly outperforms the state-of-the-art methods and verify its efficacy through extensive ablation studies.