BoxMask: Revisiting Bounding Box Supervision for Video Object Detection

Khurram Azeem Hashmi, Alain Pagani, Didier Stricker, Muhammad Zeshan Afzal

Abstract: We present a new, simple yet effective approach to up- lift video object detection. We observe that prior works operate on instance-level feature aggregation that imminently neglects the refined pixel-level representation, resulting in confusion among objects sharing similar appearance or motion characteristics. To address this limitation, we propose BoxMask, which effectively learns discriminative representations by incorporating class-aware pixel-level information. We simply consider bounding box-level annotations as a coarse mask for each object to supervise our method. The proposed module can be effortlessly integrated into any region-based detector to boost detection. Extensive experiments on ImageNet VID and EPIC KITCHENS datasets demonstrate consistent and significant improvement when we plug our BoxMask module into numerous recent state-of-the-art methods. The code will be available at https://github.com/khurramHashmi/BoxMask.