找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
樓主: 誤解
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
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-22 06:17
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
永泰县| 赫章县| 垣曲县| 德庆县| 剑川县| 长治市| 延寿县| 大化| 昭苏县| 文安县| 夏河县| 绥化市| 苏尼特左旗| 丹凤县| 庆阳市| 当阳市| 永顺县| 花垣县| 咸宁市| 西青区| 宝坻区| 潼关县| 西峡县| 荣昌县| 阳信县| 济源市| 达拉特旗| 重庆市| 新昌县| 怀集县| 柳林县| 东丰县| 康乐县| 新宾| 邢台市| 银川市| 祁东县| 鹤庆县| 漯河市| 体育| 富民县|