Two-phase weakly supervised object detection with pseudo ground truth mining

TL;DR

This paper introduces a two-phase weakly supervised object detection framework that leverages pseudo ground truth mining and refinement strategies, significantly improving detection accuracy on PASCAL VOC 2007.

Contribution

It proposes a novel two-phase WSOD architecture with a new pseudo ground truth generation and refinement method, achieving state-of-the-art performance.

Findings

Improved mAP from 49.17% to 55.23% on PASCAL VOC 2007.

Pseudo ground truth generation with multiple bounding boxes enhances learning.

Refinement strategies further boost detection accuracy.

Abstract

Weakly Supervised Object Detection (WSOD), aiming to train detectors with only image-level dataset, has arisen increasing attention for researchers. In this project, we focus on two-phase WSOD architecture which integrates a powerful detector with a pure WSOD model. We explore the effectiveness of some representative detectors utilized as the second-phase detector in two-phase WSOD and propose a two-phase WSOD architecture. In addition, we present a strategy to establish the pseudo ground truth (PGT) used to train the second-phase detector. Unlike previous works that regard top one bounding boxes as PGT, we consider more bounding boxes to establish the PGT annotations. This alleviates the insufficient learning problem caused by the low recall of PGT. We also propose some strategies to refine the PGT during the training of the second detector. Our strategies suspend the training in specific epoch, then refine the PGT by the outputs of the second-phase detector. After that, the algorithm continues the training with the same gradients and weights as those before suspending. Elaborate experiments are conduceted on the PASCAL VOC 2007 dataset to verify the effectiveness of our methods. As results demonstrate, our two-phase architecture improves the mAP from 49.17% to 53.21% compared with the single PCL model. Additionally, the best PGT generation strategy obtains a 0.7% mAP increment. Our best refinement strategy boosts the performance by 1.74% mAP. The best results adopting all of our methods achieve 55.231% mAP which is the state-of-the-art performance.