BoxMask: Revisiting Bounding Box Supervision for Video Object Detection

BoxMask: Revisiting Bounding Box Supervision for Video Object Detection
Khurram Azeem Hashmi, Alain Pagani, Didier Stricker, Muhammad Zeshan Afzal
In: Winter Conference on Applications of Computer Vision 2023. IEEE Winter Conference on Applications of Computer Vision (WACV-2023), January 3-8, Waikoloa, HI, USA, CVF, 2023.

Abstract:

We present a new, simple yet effective approach to uplift 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.