Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows. We allow the computer to actively identify objects, whose neural patterns cannot be explained by the current AOG. Then, the computer asks human about the unexplained objects, and uses the answers to automatically discover certain CNN patterns corresponding to the missing knowledge. We incrementally grow the AOG to encode new knowledge discovered during the active-learning process. In experiments, our method exhibits high learning efficiency. Our method uses about 1 6-1 3 of the part annotations for training, but achieves similar or better part-localization performance than fast-RCNN methods.
In contrast, to the best of our knowledge, there is no mechanism to distinguish a certain part concept from other parts of the same object. It is because 1) all the parts represent common foreground patterns among objects; 2) some parts ( the abdomen) do not have shape boundaries to identify their shape extent. Thus, up to now, people mainly extract implicit middle-level part patches @cite_8 , but it is difficult to capture explicit semantic meanings of these parts.
@cite_8
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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. Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute low-level features for entire views without considering part-level information. In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views. We introduce a novel definition of generally semantic parts, which Parts4Feature learns to detect in multiple views from different 3D shape segmentation benchmarks. A key idea of our architecture is that it transfers the ability to detect semantically meaningful parts in multiple views to learn 3D global features. Parts4Feature achieves this by combining a local part detection branch and a global feature learning branch with a shared region proposal module. The global feature learning branch aggregates the detected parts in terms of learned part patterns with a novel multi-attention mechanism, while the region proposal module enables locally and globally discriminative information to be promoted by each other. We demonstrate that Parts4Feature outperforms the state-of-the-art under three large-scale 3D shape benchmarks. In this paper, we propose a novel perception-based shape decomposition method which aims to decompose a shape into semantically meaningful parts. In addition to three popular perception rules (the Minima rule, the Short-cut rule and the Convexity rule) in shape decomposition, we propose a new rule named part-similarity rule to encourage consistent partition of similar parts. The problem is formulated as a quadratic ally constrained quadratic program (QCQP) problem and is solved by a trust-region method. Experiment results on MPEG-7 dataset show that we can get a more consistent shape decomposition with human perception compared with other state-of-the-art methods both qualitatively and quantitatively. Finally, we show the advantage of semantic parts over non-meaningful parts in object detection on the ETHZ dataset. This paper present a part-based approach for detecting objects with large variation of appearance. We extract local image patches as local features both from the object and from the background in training images to learn an object part model discriminatively. Our object part model discriminates the local features whether they are an object part or not. Based on the discrimination results, each local feature casts probabilistic votes for the object location and size which are learned from the training images. Our object part model also requires regression performance for predicting the object location and size through the voting procedure. We build such an object part model with an ensemble of randomized trees trained by splitting each tree node so as to reduce the entropy of class label distribution and the variance of object location and size. Experimental results on hand detection with large pose variation show that our approach outperforms conventional generalized Hough transform. We verified the performance on a public dataset of side-view cars.