The Experts below are selected from a list of 240 Experts worldwide ranked by ideXlab platform
Tao Wan - One of the best experts on this subject based on the ideXlab platform.
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pixel level data augmentation for Semantic image segmentation using generative adversarial networks
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Shuangting Liu, Jiaqi Zhang, Yuxin Chen, Yifan Liu, Zengchang Qin, Tao WanAbstract:Semantic segmentation is one of the basic topics in computer vision, it aims to assign Semantic Labels to every pixel of an image. Unbalanced Semantic Label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the Semantic Label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of Semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost the accuracy and be easily applicable to any other segmentation models.
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pixel level data augmentation for Semantic image segmentation using generative adversarial networks
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Shuangting Liu, Jiaqi Zhang, Yuxin Chen, Yifan Liu, Zengchang Qin, Tao WanAbstract:Semantic segmentation is one of the basic topics in computer vision, it aims to assign Semantic Labels to every pixel of an image. Unbalanced Semantic Label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the Semantic Label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of Semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost accuracy and be easily applicable to any other segmentation models.
Heng Tao Shen - One of the best experts on this subject based on the ideXlab platform.
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exploring auxiliary context discrete Semantic transfer hashing for scalable image retrieval
arXiv: Information Retrieval, 2019Co-Authors: Lei Zhu, Zi Huang, Liang Xie, Heng Tao ShenAbstract:Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of Semantic Label independence, memory and search efficiency. However, the learned hash codes are embedded with limited discriminative Semantics due to the intrinsic limitation of image representation. To address the problem, in this paper, we propose a novel hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH). The key idea is to \emph{directly} augment the Semantics of discrete image hash codes by exploring auxiliary contextual modalities. To this end, a unified hashing framework is formulated to simultaneously preserve visual similarities of images and perform Semantic transfer from contextual modalities. Further, to guarantee direct Semantic transfer and avoid information loss, we explicitly impose the discrete constraint, bit--uncorrelation constraint and bit-balance constraint on hash codes. A novel and effective discrete optimization method based on augmented Lagrangian multiplier is developed to iteratively solve the optimization problem. The whole learning process has linear computation complexity and desirable scalability. Experiments on three benchmark datasets demonstrate the superiority of DSTH compared with several state-of-the-art approaches.
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exploring auxiliary context discrete Semantic transfer hashing for scalable image retrieval
IEEE Transactions on Neural Networks, 2018Co-Authors: Lei Zhu, Zi Huang, Liang Xie, Heng Tao ShenAbstract:Unsupervised hashing can desirably support scalable content-based image retrieval for its appealing advantages of Semantic Label independence, memory, and search efficiency. However, the learned hash codes are embedded with limited discriminative Semantics due to the intrinsic limitation of image representation. To address the problem, in this paper, we propose a novel hashing approach, dubbed as discrete Semantic transfer hashing (DSTH). The key idea is to directly augment the Semantics of discrete image hash codes by exploring auxiliary contextual modalities. To this end, a unified hashing framework is formulated to simultaneously preserve visual similarities of images and perform Semantic transfer from contextual modalities. Furthermore, to guarantee direct Semantic transfer and avoid information loss, we explicitly impose the discrete constraint, bit-uncorrelation constraint, and bit-balance constraint on hash codes. A novel and effective discrete optimization method based on augmented Lagrangian multiplier is developed to iteratively solve the optimization problem. The whole learning process has linear computation complexity and desirable scalability. Experiments on three benchmark data sets demonstrate the superiority of DSTH compared with several state-of-the-art approaches.
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exploiting depth from single monocular images for object detection and Semantic segmentation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Yuanzhouhan Cao, Chunhua Shen, Heng Tao ShenAbstract:Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision including object detection and Semantic segmentation. Although depth sensors such as the Microsoft Kinect have facilitated easy acquisition of such depth information, the vast majority of images used in vision tasks do not contain depth information. In this paper, we show that augmenting RGB images with estimated depth can also improve the accuracy of both object detection and Semantic segmentation. Specifically, we first exploit the recent success of depth estimation from monocular images and learn a deep depth estimation model. Then we learn deep depth features from the estimated depth and combine with RGB features for object detection and Semantic segmentation. Additionally, we propose an RGB-D Semantic segmentation method which applies a multi-task training scheme: Semantic Label prediction and depth value regression. We test our methods on several datasets and demonstrate that incorporating information from estimated depth improves the performance of object detection and Semantic segmentation remarkably.
Qi Tian - One of the best experts on this subject based on the ideXlab platform.
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Semantic subspace projection and its applications in image retrieval
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: Qi TianAbstract:One of the most challenging problems for image retrieval applications is to find the optimal mapping between high-level Semantic concept and low-level features. Traditional approaches often assume that images with same Semantic Label share strong visual similarities and should be clustered together to facilitate modeling and classification. Our research indicates this assumption is inappropriate in many cases. Instead we model the images as lying on nonlinear image subspaces embedded in the high-dimensional feature space and find that multiple subspaces may correspond to one Semantic concept. By intelligently utilizing the similarity and dissimilarity information in Semantic and geometric (image) domains, we propose an optimal Semantic subspace projection (SSP) that captures the most important properties of the subspaces with respect to classification. Theoretical analysis proves that the well-known linear discriminant analysis (LDA) could be formulated as a special case of SSP. To capture the Semantic concept dynamically, SSP can integrate relevance feedback efficiently through incremental learning. Kernel SSP is further proposed to handle nonlinearly separable data. Extensive experiments have been designed and conducted to compare our proposed method to the state-of-the-art techniques such as LDA, locality preservation projection (LPP), local linear embedding (LLE), local discriminant embedding (LDE) and their variants. The results show the superior performance of SSP.
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learning image manifolds by Semantic subspace projection
ACM Multimedia, 2006Co-Authors: Qi TianAbstract:In many image retrieval applications, the mapping between high-level Semantic concept and low-level features is obtained through a learning process. Traditional approaches often assume that images with same Semantic Label share strong visual similarities and should be clustered together to facilitate modeling and classification. Our research indicates this assumption is inappropriate in many cases. Instead we model the images as lying on non-linear image subspaces embedded in the high-dimensional space and find that multiple subspaces may correspond to one Semantic concept.
Shuangting Liu - One of the best experts on this subject based on the ideXlab platform.
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pixel level data augmentation for Semantic image segmentation using generative adversarial networks
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Shuangting Liu, Jiaqi Zhang, Yuxin Chen, Yifan Liu, Zengchang Qin, Tao WanAbstract:Semantic segmentation is one of the basic topics in computer vision, it aims to assign Semantic Labels to every pixel of an image. Unbalanced Semantic Label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the Semantic Label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of Semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost the accuracy and be easily applicable to any other segmentation models.
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pixel level data augmentation for Semantic image segmentation using generative adversarial networks
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Shuangting Liu, Jiaqi Zhang, Yuxin Chen, Yifan Liu, Zengchang Qin, Tao WanAbstract:Semantic segmentation is one of the basic topics in computer vision, it aims to assign Semantic Labels to every pixel of an image. Unbalanced Semantic Label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the Semantic Label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of Semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost accuracy and be easily applicable to any other segmentation models.
Chris Develder - One of the best experts on this subject based on the ideXlab platform.
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knowledge base population using Semantic Label propagation
Knowledge Based Systems, 2016Co-Authors: Lucas Sterckx, Thomas Demeester, Johannes Deleu, Chris DevelderAbstract:Training relation extractors for the purpose of automated knowledge base population requires the availability of sufficient training data. The amount of manual Labeling can be significantly reduced by applying distant supervision, which generates training data by aligning large text corpora with existing knowledge bases. This typically results in a highly noisy training set, where many training sentences do not express the intended relation. In this paper, we propose to combine distant supervision with minimal human supervision by annotating features (in particular shortest dependency paths) rather than complete relation instances. Such feature Labeling eliminates noise from the initial training set, resulting in a significant increase of precision at the expense of recall. We further improve on this approach by introducing the Semantic Label Propagation (SLP) method, which uses the similarity between low-dimensional representations of candidate training instances to again extend the (filtered) training set in order to increase recall while maintaining high precision. Our strategy is evaluated on an established test collection designed for knowledge base population (KBP) from the TAC KBP English slot filling task. The experimental results show that SLP leads to substantial performance gains when compared to existing approaches while requiring an almost negligible human annotation effort.
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Knowledge Base Population using Semantic Label Propagation
arXiv:1511.06219 [cs], 2015Co-Authors: Lucas Sterckx, Julie Deleu, Thomas Demeester, Chris DevelderAbstract:A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual Labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature Labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.
