End-to-End Weakly Supervised Object Detection with Sparse Proposal Evolution.

Conventional methods for weakly supervised object detection (WSOD) typically enumerate dense proposals and select the discriminative proposals as objects. However, these two-stage “enumerate-and-select” methods suffer object feature ambiguity brought by dense proposals and low detection efficiency caused by the proposal enumeration procedure. In this study, we propose a sparse proposal evolution (SPE) approach, which advances WSOD from the two-stage pipeline with dense proposals to an end-to-end framework with sparse proposals. SPE is built upon a visual transformer equipped with a seed proposal generation (SPG) branch and a sparse proposal refinement (SPR) branch. SPG generates high-quality seed proposals by taking advantage of the cascaded self-attention mechanism of the visual transformer, and SPR trains the detector to predict sparse proposals which are supervised by the seed proposals in a one-to-one matching fashion. SPG and SPR are iteratively performed so that seed proposals update to accurate supervision signals and sparse proposals evolve to precise object regions. Experiments on VOC and COCO object detection datasets show that SPE outperforms the state-of-the-art end-to-end methods by 7.0% mAP and 8.1% AP50. It is an order of magnitude faster than the two-stage methods, setting the first solid baseline for end-to-end WSOD with sparse proposals. The code is available at https://github.com/MingXiangL/SPE.