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Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not...
Textual-visual matching aims at measuring similarities between sentence descriptions and images. Most existing methods tackle this problem without effectively utilizing identity-level annotations. In this paper, we propose an identity-aware two-stage framework for the textual-visual matching problem. Our stage-1 CNN-LSTM network learns to embed cross-modal features with a novel Cross-Modal Cross-Entropy...
Sequences of duplicate code, either with or without modification, are known as code clones or just clones. Code clones are generally considered undesirable for a number of reasons, although they can offer some convenience to developers. The detection of code clones helps to improve the quality of source code through software re-engineering. Numerous methods have been proposed for code clone detection...
In this paper, we propose to incorporate convolutional neural networks with a multi-context attention mechanism into an end-to-end framework for human pose estimation. We adopt stacked hourglass networks to generate attention maps from features at multiple resolutions with various semantics. The Conditional Random Field (CRF) is utilized to model the correlations among neighboring regions in the attention...
The so called Smart Grid is said to be enabled with bi-direction communication networks and energy flow. IEC 61850 is a substation communication standard which improves interoperability of the substation design. IEC 61499 is an automation standard which can be used for implementation of control in IEC 61850 models. This work is utilizing Model Driven Engineering techniques for substation automation...
This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as “image cosegmentation,” iterates to extract consistent regions on images and jointly refines...
This article investigates a data-driven approach for semantic scene understanding, without pixelwise annotation or classifier training. The proposed framework parses a target image in two steps: first, retrieving its exemplars (that is, references) from an image database, where all images are unsegmented but annotated with tags; second, recovering its pixel labels by propagating semantics from the...
The second edition of the IEC 61499 standard aims to clarify the interpretation ambiguities of function block's execution semantics. This resolves the pivotal issue of realizing portable and interoperable implementations of the IEC 61499 reference architecture. As the IEC 61499 standard is about entering its technology takeoff phase, these clarifications are timely and important. It is hence expected...
This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre-training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references....
The descriptive power of low-level image features for describing the high-level semantic concepts is limited for content-based image retrieval (CBIR). To reduce this semantic gap and improve retrieval performance of CBIR, a distance metric learning method is proposed which can learn a linear projection to define a distance metric for maximizing mean average precision (MAP). The smooth approximation...
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