论文浏览

【论文题目】Weakly supervised detection with decoupled attention-based deep representation

【作    者】Wenhui Jiang, Zhicheng Zhao, Fei Su        点击下载PDF全文

【关 键 字】Weak supervision, Object detection, Deep learning, Attention model

【发表刊物/会议】
    Multimedia Tools and Applications

【摘    要】
     Training object detectors with only image-level annotations is an important problem with a variety of applications. However, due to the deformable nature of objects, a target object delineated by a bounding box always includes irrelevant context and occlusions, which causes large intra-class object variations and ambiguity in object-background distinction. For this reason, identifying the object of interest from a substantial amount of cluttered backgrounds is very challenging. In this paper, we propose a decoupled attention-based deep model to optimize region-based object representation. Different from existing approaches posing object representation in a single-tower model, our proposed network decouples object representation into two separate modules, i.e., image representation and attention localization. The image representation module captures content-based semantic representation, while the attention localization module regresses an attention map which simultaneously highlights the locations of the discriminative object parts and down weights the irrelevant backgrounds presented in the image. The combined representation alleviates the impact from the noisy context and occlusions inside an object bounding box. As a result, object-background ambiguity can be largely reduced and background regions can be suppressed effectively. In addition, the proposed object representation model can be seamlessly integrated into a state-of-the-art weakly supervised detection framework, and the entire model can be trained end-to-end. We extensively evaluate the detection performance on the PASCAL VOC 2007, VOC 2010 and VOC2012 datasets. Experimental results demonstrate that our approach effectively improves weakly supervised object detection.

【发 表 年】2017

【发 表 月】8

【类    别】计算机视觉


Tel: 086-010-62283118 邮编:100876
地址:北京市海淀区西土城路10号北京邮电大学教二楼多媒体中心
北京市海淀区西土城路十号113#信箱
版权所有:北京邮电大学多媒体通信与模式识别研究室 京ICP证14002347号