EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement

• URL: http://arxiv.org/abs/2002.07421v1
• Date: Tue, 18 Feb 2020 08:04:58 GMT
• Title: EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
• Authors: Linpu Fang, Hang Xu, Zhili Liu, Sarah Parisot, Zhenguo Li
• Abstract summary: We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
• Score: 53.69674636044927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detectors have much lower performance and cannot be used reliably in a realistic setting. In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels. State of the art methods typically propose an iterative approach, alternating between generating pseudo-labels and updating a detector. This paradigm requires careful manual hyper-parameter tuning for mining good pseudo labels at each round and is quite time-consuming. To address these issues, we present EHSOD, an end-to-end hybrid-supervised object detection system which can be trained in one shot on both fully and weakly-annotated data. Specifically, based on a two-stage detector, we proposed two modules to fully utilize the information from both kinds of labels: 1) CAMRPN module aims at finding foreground proposals guided by a class activation heat-map; 2) hybrid-supervised cascade module further refines the bounding-box position and classification with the help of an auxiliary head compatible with image-level data. Extensive experiments demonstrate the effectiveness of the proposed method and it achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data, e.g. 37.5% mAP on COCO. We will release the code and the trained models.

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