| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Umar Asif, Mohammed Bennamoun, Ferdous A Sohe. A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 9. 2019-11-20. PMID:28866483. |
this is achieved through three postulates: 1) spatial invariance $-$ this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability $-$ this is achieved by introducing fisher encoding within the cnn architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multi-modal hierarchical fusion$-$ this is achieved through the regularization of semantic segmentation to a multi-modal cnn architecture, where class probabilities are estimated at different hierarchical levels (i.e., image- and pixel-levels), and fused into a conditional random field (crf)-based inference hypothesis, the optimization of which produces consistent class labels in rgb-d images. |
2019-11-20 |
2023-08-13 |
Not clear |
| Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai W. A Semantic-Based Method for Visualizing Large Image Collections. IEEE transactions on visualization and computer graphics. vol 25. issue 7. 2019-11-20. PMID:29993720. |
the semantic information extractor employs an image captioning technique based on convolutional neural network (cnn) to produce descriptive captions for images, which can be transformed into semantic keywords. |
2019-11-20 |
2023-08-13 |
Not clear |
| Yalong Jiang, Zheru Ch. A CNN Model for Semantic Person Part Segmentation with Capacity Optimization. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30571629. |
a cnn model for semantic person part segmentation with capacity optimization. |
2019-11-20 |
2023-08-13 |
Not clear |
| Carolina Redondo-Cabrera, Marcos Baptista-Rios, Roberto J Lopez-Sastr. Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30802862. |
training a convolutional neural network (cnn) for semantic segmentation typically requires to collect a large amount of accurate pixel-level annotations, a hard and expensive task. |
2019-11-20 |
2023-08-13 |
Not clear |
| Zitian Cheny, Yanwei Fuy, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, Leonid Siga. Multi-level Semantic Feature Augmentation for One-shot Learning. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30969924. |
the encoder part of the trinet learns to map multi-layer visual features from cnn to a semantic vector. |
2019-11-20 |
2023-08-13 |
Not clear |
| Zhongliang Yang, Yongfeng Huang, Yiran Jiang, Yuxi Sun, Yu-Jin Zhang, Pengcheng Lu. Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network. Scientific reports. vol 8. issue 1. 2019-11-04. PMID:29679019. |
in this study, we present a clinical intelligent decision approach based on convolutional neural networks(cnn), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. |
2019-11-04 |
2023-08-13 |
Not clear |
| Jingwen Ye, Yongcheng Jing, Xinchao Wang, Kairi Ou, Dacheng Tao, Mingli Son. Edge-Sensitive Human Cutout with Hierarchical Granularity and Loopy Matting Guidance. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-09-19. PMID:31502968. |
our model comprises two parts, the extensible hierarchical semantic segmentation block using cnn and the matting module composed of guided filters. |
2019-09-19 |
2023-08-13 |
human |
| Jingwen Ye, Yongcheng Jing, Xinchao Wang, Kairi Ou, Dacheng Tao, Mingli Son. Edge-Sensitive Human Cutout with Hierarchical Granularity and Loopy Matting Guidance. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-09-19. PMID:31502968. |
the obtained matting map is then in turn fed back to the cnn in the first block for refining the semantic segmentation results. |
2019-09-19 |
2023-08-13 |
human |
| Bolei Zhou, David Bau, Aude Oliva, Antonio Torralb. Interpreting Deep Visual Representations via Network Dissection. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 9. 2019-09-11. PMID:30040625. |
the proposed method quantifies the interpretability of cnn representations by evaluating the alignment between individual hidden units and visual semantic concepts. |
2019-09-11 |
2023-08-13 |
Not clear |
| Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Ruecker. DRINet for Medical Image Segmentation. IEEE transactions on medical imaging. vol 37. issue 11. 2019-08-19. PMID:29993738. |
the u-net architecture is one of the most well-known cnn architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. |
2019-08-19 |
2023-08-13 |
Not clear |
| Yaojun Dai, Shiju Yan, Bin Zheng, Chengli Son. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification. Physics in medicine and biology. vol 63. issue 24. 2019-08-01. PMID:30524071. |
image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convolutional neural network (cnn). |
2019-08-01 |
2023-08-13 |
Not clear |
| Yiling Wu, Shuhui Wang, Guoli Song, Qingming Huan. Online Asymmetric Metric Learning With Multi-Layer Similarity Aggregation for Cross-Modal Retrieval. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 9. 2019-07-03. PMID:30951466. |
considering that multiple cnn feature layers naturally represent visual information from low-level visual patterns to high-level semantic abstraction, we propose a new asymmetric image-text similarity formulation which aggregates the layer-wise visual-textual similarities parameterized by different bilinear parameter matrices. |
2019-07-03 |
2023-08-13 |
Not clear |
| Davis M Vigneault, Weidi Xie, Carolyn Y Ho, David A Bluemke, J Alison Nobl. Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks. Medical image analysis. vol 48. 2019-06-06. PMID:29857330. |
here, we present Ω-net (omega-net): a novel convolutional neural network (cnn) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. |
2019-06-06 |
2023-08-13 |
human |
| Mingliang Fu, Weijia Zho. DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation. Sensors (Basel, Switzerland). vol 19. issue 5. 2019-03-04. PMID:30823451. |
for the latter, intermediate cues, such as 3d object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by cnn in the first stage. |
2019-03-04 |
2023-08-13 |
Not clear |
| R Guerrero, C Qin, O Oktay, C Bowles, L Chen, R Joules, R Wolz, M C Valdés-Hernández, D A Dickie, J Wardlaw, D Ruecker. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage. Clinical. vol 17. 2019-01-30. PMID:29527496. |
the proposed fully convolutional cnn architecture, called uresnet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. |
2019-01-30 |
2023-08-13 |
human |
| Jinzheng Cai, Le Lu, Zizhao Zhang, Fuyong Xing, Lin Yang, Qian Yi. Pancreas Segmentation in MRI using Graph-Based Decision Fusion on Convolutional Neural Networks. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. vol 9901. 2018-12-11. PMID:28083570. |
our approach conducts pancreatic detection and boundary segmentation with two types of cnn models respectively: 1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; 2) the boundary detection step to allocate the semantic boundaries of pancreas. |
2018-12-11 |
2023-08-13 |
Not clear |
| Hang Li, Xiu-Jun Gong, Hua Yu, Chang Zho. Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences. Molecules (Basel, Switzerland). vol 23. issue 8. 2018-11-05. PMID:30071670. |
the different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, cnn and lstm layers, respectively. |
2018-11-05 |
2023-08-13 |
human |
| Lei Zhang, Xiantong Zhen, Ling Shao, Jingkuan Son. Learning Match Kernels on Grassmann Manifolds for Action Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 1. 2018-09-24. PMID:30136940. |
specifically, we propose modeling actions as a linear subspace on the grassmann manifold; the subspace is a set of convolutional neural network (cnn) feature vectors pooled temporally over frames in semantic video clips, which simultaneously captures local discriminant patterns and temporal dynamics of motion. |
2018-09-24 |
2023-08-13 |
Not clear |
| Jufeng Yang, Jie Liang, Hui Shen, Kai Wang, Paul L Rosin, Ming-Hsuan Yan. Dynamic Match Kernel With Deep Convolutional Features for Image Retrieval. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 27. issue 11. 2018-07-31. PMID:29994213. |
although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (cnn). |
2018-07-31 |
2023-08-13 |
Not clear |
| Mengmeng Zhang, Wei Li, Qian D. Diverse Region-Based CNN for Hyperspectral Image Classification. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 27. issue 6. 2018-07-30. PMID:29533899. |
in this paper, we propose a classification framework, called diverse region-based cnn, which can encode semantic context-aware representation to obtain promising features. |
2018-07-30 |
2023-08-13 |
Not clear |