Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level. Based upon previous work on part localization, in this paper, we address the problem of inferring rich semantics imparted by an object part in still images. We propose to tokenize the semantic space as a discrete set of part states. Our modeling of part state is spatially localized, therefore, we formulate the part state inference problem as a pixel-wise annotation problem. An iterative part-state inference neural network is specifically designed for this task, which is efficient in time and accurate in performance. Extensive experiments demonstrate that the proposed method can effectively predict the semantic states of parts and simultaneously correct localization errors, thus benefiting a few visual understanding applications. The other contribution of this paper is our part state dataset which contains rich part-level semantic annotations.
Object parts layout has been used to provide sub-object level information. The specific problem closely related to object parts detection is human pose estimation @cite_16 @cite_7 @cite_22 where different human parts (e.g., head, body, hands, legs) need to be localized. @cite_26 , a separate representation was respectively proposed for holistic object and body parts, and a fully connected model was used to optimize their arrangement. The model was applied to the six animal categories and achieved a better object representation performance. @cite_17 , to segment object parts, a mixture of compositional models was used to represent the object boundary and the boundaries of the semantic parts. This compositional model incorporates edge, appearance, and semantic parts. The above methods localize parts only, but not in-depth explore semantics on them.
@cite_26 @cite_22 @cite_7 @cite_16 @cite_17
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In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of semantic parts. And we incorporate edge, appearance, and semantic part cues into the compositional model. Given part-level segmentation annotation, we develop a novel algorithm to learn a mixture of compositional models under various poses and viewpoints for certain animal classes. Furthermore, a linear complexity algorithm is offered for efficient inference of the compositional model using dynamic programming. We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method. Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different "detectability" patterns caused by deformations, occlusion and or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1 AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc.), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part. We integrate spatial part layout into supervised learning of the deep ConvNets.Spatial learning for unsupervised pre-training of the deep ConvNets is proposed.We are the first to apply deep learning to the body segmentation from depth images. Object recognition, human pose estimation and scene recognition are applications which are frequently solved through a decomposition into a collection of parts. The resulting local representation has significant advantages, especially in the case of occlusions and when the subject is non-rigid. Detection and recognition require modelling the appearance of the different object parts as well as their spatial layout. This representation has been particularly successful in body part estimation from depth images.Integrating the spatial layout of parts may require the minimization of complex energy functions. This is prohibitive in most real world applications and therefore often omitted. However, ignoring the spatial layout puts all the burden on the classifier, whose only available information is local appearance. We propose a new method to integrate spatial layout into parts classification without costly pairwise terms during testing. Spatial relationships are exploited in the training algorithm, but not during testing. As with competing methods, the proposed method classifies pixels independently, which makes real-time processing possible. We show that training a classifier with spatial relationships increases generalization performance when compared to classical training minimizing classification error on the training set. We present an application to human body part estimation from depth images. Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. head, body and arms, etc.) from natural images is a challenging and fundamental problem in computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle this difficulty, we propose a Hierarchical Auto-Zoom Net (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of two Auto-Zoom Nets (AZNs), each employing fully convolutional networks for two tasks: (1) predict the locations and scales of object instances (the first AZN) or their parts (the second AZN); (2) estimate the part scores for predicted object instance or part regions. Our model can adaptively zoom (resize) predicted image regions into their proper scales to refine the parsing. We conduct extensive experiments over the PASCAL part datasets on humans, horses, and cows. In all the three categories, our approach significantly outperforms alternative state-of-the-arts by more than (5 , ) mIOU and is especially better at segmenting small instances and small parts. In summary, our strategy of first zooming into objects and then zooming into parts is very effective. It also enables us to process different regions of the image at different scales adaptively so that we do not need to waste computational resources scaling the entire image. Most objects are composed of parts which have a semantic meaning. Ahandle can have many different shapes and can be present in quite different objects, but there is only one semantic meaning to a handle, which is "a part that is designed especially to be grasped by the hand". We introduce here a novel 3D algorithm named CPS for the decomposition of objects into their semantically meaningful parts. These meaningful parts are learned from experiments where robot grasps different objects. Objects are represented in compositional graph hierarchy where their parts are represented as the relationship between subparts, which are in turn represented based on the relationships between small adjacent regions. Unlike other compositional approaches, our method relies on learning semantically meaningful parts which are learned from grasping experience. This compositional part representation provides generalization for part segmentation. We evaluated our method in this respect, by training ion one dataset and evaluating it on another. We achieved on average78 part overlap accuracy for segmentation of novel part instances.