最近一周的目标检测论文

上传者: 43537420 | 上传时间: 2022-10-18 17:05:49 | 文件大小: 25.21MB | 文件类型: ZIP
最近一周的博客所读论文汇总,一共9篇

文件下载

资源详情

[{"title":"( 10 个子文件 25.21MB ) 最近一周的目标检测论文","children":[{"title":"object detection","children":[{"title":"A survey and performance evaluation of deep learning methods for small.pdf <span style='color:#111;'> 1.78MB </span>","children":null,"spread":false},{"title":"Gliding_Vertex_on_the_Horizontal_Bounding_Box_for_Multi-Oriented_Object_Detection.pdf <span style='color:#111;'> 3.56MB </span>","children":null,"spread":false},{"title":"已读","children":[{"title":"Tiny Object Detection in Aerial Images.pdf <span style='color:#111;'> 1.99MB </span>","children":null,"spread":false}],"spread":true},{"title":"Depthwise_Nonlocal_Module_for_Fast_Salient_Object_Detection_Using_a_Single_Thread.pdf <span style='color:#111;'> 2.10MB </span>","children":null,"spread":false},{"title":"Deep_Regionlets_Blended_Representation_and_Deep_Learning_for_Generic_Object_Detection.pdf <span style='color:#111;'> 1.17MB </span>","children":null,"spread":false},{"title":"Deep_Affinity_Network_for_Multiple_Object_Tracking.pdf <span style='color:#111;'> 3.24MB </span>","children":null,"spread":false},{"title":"Maritime_Environment_Perception_Based_on_Deep_Learning.pdf <span style='color:#111;'> 9.12MB </span>","children":null,"spread":false},{"title":"Lightweight_Deep_Neural_Network_for_Joint_Learning_of_Underwater_Object_Detection_and_Color_Conversion.pdf <span style='color:#111;'> 3.37MB </span>","children":null,"spread":false},{"title":"Object Detection Using Deep Learning Methods.pdf <span style='color:#111;'> 1.76MB </span>","children":null,"spread":false},{"title":"Transferable_Interactiveness_Knowledge_for_Human-Object_Interaction_Detection.pdf <span style='color:#111;'> 2.12MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明