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[bibtex]@InProceedings{Hashmi_2023_WACV, author = {Hashmi, Khurram Azeem and Pagani, Alain and Stricker, Didier and Afzal, Muhammad Zeshan}, title = {BoxMask: Revisiting Bounding Box Supervision for Video Object Detection}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {2030-2040} }
BoxMask: Revisiting Bounding Box Supervision for Video Object Detection
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 pro- pose 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.
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