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Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

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41#
發(fā)表于 2025-3-28 17:50:40 | 只看該作者
Gated Contiguous Memory U-Net for Single Image Dehazinget like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem. The contiguous memory residual block is used to increase the flow of information by feature reusing and a gated fusion sub-network module is used to better
42#
發(fā)表于 2025-3-28 19:56:07 | 只看該作者
Combined Correlation Filters with Siamese Region Proposal Network for Visual Trackingal Network (SiamRPN) tracker can get more accurate bounding box with proposal refinement, yet, most siamese trackers are lack of discrimination without target classification and robustness without online learning module. To tackle the problem, in this paper, we propose an ensemble tracking framework
43#
發(fā)表于 2025-3-29 02:14:17 | 只看該作者
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instrumentsruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It ca
44#
發(fā)表于 2025-3-29 03:53:12 | 只看該作者
A Novel Image-Based Malware Classification Model Using Deep Learningthis paper, we propose a novel image-based malware classification model using deep learning to counter large-scale malware analysis. The model includes a malware embedding method called YongImage which maps instruction-level information and disassembly metadata generated by IDA disassembler tool int
45#
發(fā)表于 2025-3-29 09:53:38 | 只看該作者
Visual Saliency Detection via Convolutional Gated Recurrent Units frameworks is still an open problem. Recent saliency detection models designed using complex Deep Neural Networks to extract robust features, however often fail to select the right contextual features. These methods generally utilize physical attributes of objects for generating final saliency maps
46#
發(fā)表于 2025-3-29 13:33:40 | 只看該作者
47#
發(fā)表于 2025-3-29 19:08:06 | 只看該作者
48#
發(fā)表于 2025-3-29 22:08:04 | 只看該作者
Reinforcing LiDAR-Based 3D Object Detection with RGB and 3D Informationation and 3D boxes regression. However, some background and foreground objects may have similar appearances in point clouds. Therefore the accuracy of LiDAR-based 3D object detection is hard to be improved. In this paper, we propose a three-stage 3D object detection method called RGB3D to reinforce
49#
發(fā)表于 2025-3-29 23:59:48 | 只看該作者
50#
發(fā)表于 2025-3-30 04:34:14 | 只看該作者
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