| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Panayu Keelawat, Nattapong Thammasan, Masayuki Numao, Boonserm Kijsiriku. A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN. Sensors (Basel, Switzerland). vol 21. issue 5. 2021-04-27. PMID:33804366. |
the temporal information in distinct window sizes was found to significantly affect the recognition performance, and cnn was found to be more responsive to changing window sizes than the support vector machine. |
2021-04-27 |
2023-08-13 |
human |
| Lu Jin, Zechao Li, Jinhui Tan. Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals. IEEE transactions on neural networks and learning systems. vol PP. 2020-06-10. PMID:32502968. |
the proposed deep hashing framework leverages 2-d convolutional neural networks (cnn) as the backbone network to capture the spatial information for image-text retrieval, while the 3-d cnn as the backbone network to capture the spatial and temporal information for video-text retrieval. |
2020-06-10 |
2023-08-13 |
Not clear |
| Jinmin Xu, Huafeng Li. Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging. Physics in medicine and biology. vol 64. issue 18. 2020-04-22. PMID:31292287. |
combined with volumetric dual-tracer images, we adopted a three-dimensional (3d) convolutional neural network (cnn) to learn full features, including spatial information and temporal information simultaneously. |
2020-04-22 |
2023-08-13 |
Not clear |
| Dongyi Kim, Hyeon Cho, Hochul Shin, Soo-Chul Lim, Wonjun Hwan. An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video. Sensors (Basel, Switzerland). vol 19. issue 16. 2020-02-25. PMID:31426463. |
in this paper, we proposed a bottleneck-based 3d depthwise separable cnn architecture where the video is disentangled into spatial and temporal information. |
2020-02-25 |
2023-08-13 |
Not clear |
| Yueming Jin, Qi Dou, Hao Chen, Lequan Yu, Jing Qin, Chi-Wing Fu, Pheng-Ann Hen. SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network. IEEE transactions on medical imaging. vol 37. issue 5. 2019-04-24. PMID:29727275. |
different from previous methods which harness visual and temporal information separately, the proposed sv-rcnet seamlessly integrates a convolutional neural network (cnn) and a recurrent neural network (rnn) to form a novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos. |
2019-04-24 |
2023-08-13 |
Not clear |
| Dat Tien Nguyen, Tuyen Danh Pham, Min Beom Lee, Kang Ryoung Par. Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information. Sensors (Basel, Switzerland). vol 19. issue 2. 2019-02-08. PMID:30669531. |
to address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (cnn)-recurrent neural network (rnn) along with handcrafted features. |
2019-02-08 |
2023-08-13 |
human |