HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
<|reference_start|>HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection: Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum. In this paper, we introduce a unified, high-capacity weakly supervised object detection (WSOD) network called HUWSOD, which utilizes a comprehensive self-training framework without needing external modules or additional supervision. HUWSOD innovatively incorporates a self-supervised proposal generator and an autoencoder proposal generator with a multi-rate resampling pyramid to replace traditional object proposals, enabling end-to-end WSOD training and inference. Additionally, we implement a holistic self-training scheme that refines detection scores and coordinates through step-wise entropy minimization and consistency-constraint regularization, ensuring consistent predictions across stochastic augmentations of the same image. Extensive experiments on PASCAL VOC and MS COCO demonstrate that HUWSOD competes with state-of-the-art WSOD methods, eliminating the need for offline proposals and additional data. The peak performance of HUWSOD approaches that of fully-supervised Faster R-CNN. Our findings also indicate that randomly initialized boxes, although significantly different from well-designed offline object proposals, are effective for WSOD training.<|reference_end|>
@article{cao2024huwsod:,
    title={HUWSOD: Holistic Self-training for Unified Weakly Supervised Object
  Detection},
    author={Liujuan Cao, Jianghang Lin, Zebo Hong, Yunhang Shen, Shaohui Lin, Chao
  Chen, Rongrong Ji},
    journal={arXiv preprint arXiv:2406.19394},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19394},
    primaryClass={cs.CV}
}