Self-Classification Enhancement and Correction for Weakly Supervised Object Detection

• URL: http://arxiv.org/abs/2505.16294v1
• Date: Thu, 22 May 2025 06:45:58 GMT
• Title: Self-Classification Enhancement and Correction for Weakly Supervised Object Detection
• Authors: Yufei Yin, Lechao Cheng, Wengang Zhou, Jiajun Deng, Zhou Yu, Houqiang Li,
• Abstract summary: weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost.<n>In this work, we introduce a novel WSOD framework to ameliorate these two issues.<n>For one thing, we propose a self-classification enhancement module that integrates intra-class binary classification (ICBC) to bridge the gap between the two distinct MCC tasks.<n>For another, we propose a self-classification correction algorithm during inference, which combines the results of both MCC tasks to effectively reduce the mis-classified predictions.
• Score: 113.51483527300496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two-stage multi-class classification (MCC) task, i.e., multiple instance learning and online classification refinement. Despite achieving non-trivial progresses, these methods overlook potential classification ambiguities between these two MCC tasks and fail to leverage their unique strengths. In this work, we introduce a novel WSOD framework to ameliorate these two issues. For one thing, we propose a self-classification enhancement module that integrates intra-class binary classification (ICBC) to bridge the gap between the two distinct MCC tasks. The ICBC task enhances the network's discrimination between positive and mis-located samples in a class-wise manner and forges a mutually reinforcing relationship with the MCC task. For another, we propose a self-classification correction algorithm during inference, which combines the results of both MCC tasks to effectively reduce the mis-classified predictions. Extensive experiments on the prevalent VOC 2007 & 2012 datasets demonstrate the superior performance of our framework.

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