Modulating Localization and Classification for Harmonized Object Detection

• URL: http://arxiv.org/abs/2103.08958v1
• Date: Tue, 16 Mar 2021 10:36:02 GMT
• Title: Modulating Localization and Classification for Harmonized Object Detection
• Authors: Taiheng Zhang, Qiaoyong Zhong, Shiliang Pu, Di Xie
• Abstract summary: We propose a mutual learning framework to modulate the two tasks. In particular, the two tasks are forced to learn from each other with a novel mutual labeling strategy. We achieve a significant performance gain over the baseline detectors on the COCO dataset.
• Score: 40.82723262074911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection involves two sub-tasks, i.e. localizing objects in an image and classifying them into various categories. For existing CNN-based detectors, we notice the widespread divergence between localization and classification, which leads to degradation in performance. In this work, we propose a mutual learning framework to modulate the two tasks. In particular, the two tasks are forced to learn from each other with a novel mutual labeling strategy. Besides, we introduce a simple yet effective IoU rescoring scheme, which further reduces the divergence. Moreover, we define a Spearman rank correlation-based metric to quantify the divergence, which correlates well with the detection performance. The proposed approach is general-purpose and can be easily injected into existing detectors such as FCOS and RetinaNet. We achieve a significant performance gain over the baseline detectors on the COCO dataset.

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