DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization

By Jun Zhu, Xianjie Chen, Alan L. Yuille. 2015

23 citations recorded by the source corpus.

In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization. For this purpose, we annotate semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset, which provides information on object pose, occlusion, viewpoint and functionality. DeePM is a latent graphical model based on the state-of-the-art R-CNN framework, which learns an explicit representation of the object-part configuration with flexible type sharing (e.g., a sideview horse head can be shared by a fully-visible sideview horse and a highly truncated sideview horse with head and neck only). For comparison, we also present an end-to-end Object-Part (OP) R-CNN which learns an implicit feature representation for jointly mapping an image ROI to the object and part bounding boxes. We evaluate the proposed methods for both the object and part detection performance on PASCAL VOC 2012, and show that DeePM consistently outperforms OP R-CNN in detecting objects and parts. In addition, it obtains superior performance to Fast and Faster R-CNNs in object detection.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015, methodology): “Since then, the frameworks of RPN and Faster R-CNN have been adopted and generalized to other methods, such as 3D object detection [13], part-based detection [14], instance segmentation [15], and image captioning [16].”
Part-Stacked CNN for Fine-Grained Visual Categorization (2015, background): “Recently, the best performing methods mostly sought for improvement brought by the following three aspects: more discriminative features including deep CNNs for better visual representation [5, 33, 19, 39, 37], explicit alignment approaches to eliminate pose displacements [6, 14], and part-based met”
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion (2017, methodology): “The detected parts can directly assist object detection (Felzenszwalb et al. 2010; Zhu, Chen, and Yuille 2015), be used for a ﬁne-scaled description of the object (Zhang et al. 2014), or serve as auxiliary cues to understand classiﬁcation (Huang et al. 2016), object detection or segmentation (Wang…”