  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.
