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Weakly Supervised Object Detection (WSOD) enables the training of objectdetection models using only image-level annotations. State-of-the-art WSODdetectors commonly rely on multi-instance learning (MIL) as the backbone oftheir detectors and assume that the bounding box proposals of an image areindependent of each other. However, since such approaches only utilize thehighest score proposal and discard the potentially useful information fromother proposals, their independent MIL backbone often limits models to salientparts of an object or causes them to detect only one object per class. To solvethe above problems, we propose a novel backbone for WSOD based on our tailoredVision Transformer named Weakly Supervised Transformer Detection Network(WSTDN). Our algorithm is not only the first to demonstrate that self-attentionmodules that consider inter-instance relationships are effective backbones forWSOD, but also we introduce a novel bounding box mining method (BBM) integratedwith a memory transfer refinement (MTR) procedure to utilize the instancedependencies for facilitating instance refinements. Experimental results onPASCAL VOC2007 and VOC2012 benchmarks demonstrate the effectiveness of ourproposed WSTDN and modified instance refinement modules.

Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection