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

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