  Weakly supervised learning has emerged as a compelling tool for object
detection by reducing the need for strong supervision during training. However,
major challenges remain: (1) differentiation of object instances can be
ambiguous; (2) detectors tend to focus on discriminative parts rather than
entire objects; (3) without ground truth, object proposals have to be redundant
for high recalls, causing significant memory consumption. Addressing these
challenges is difficult, as it often requires to eliminate uncertainties and
trivial solutions. To target these issues we develop an instance-aware and
context-focused unified framework. It employs an instance-aware self-training
algorithm and a learnable Concrete DropBlock while devising a memory-efficient
sequential batch back-propagation. Our proposed method achieves
state-of-the-art results on COCO ($12.1\% ~AP$, $24.8\% ~AP_{50}$), VOC 2007
($54.9\% ~AP$), and VOC 2012 ($52.1\% ~AP$), improving baselines by great
margins. In addition, the proposed method is the first to benchmark ResNet
based models and weakly supervised video object detection. Code, models, and
more details will be made available at: https://github.com/NVlabs/wetectron.
