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https://www.cnblogs.com/zhengyuqian/p/10509763.html CVPR2018 目标检测算法总览(最新的目标检测论文) 1.Cascaded RCNN：caffe 2.Relation Networks for Object Detection ：MxNet 3.RefineDet：caffe 4.Scale-Transferrable Object Detection：pytorch 5.Min-Entropy Latent Model for Weakly Supervised Object Detection：torch 6.Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection：pytorch 7.Feature Selective Networks for Object Detection： 8.Deep Reinforcement Learning of Region Proposal Networks for Object Detection:tensorflow 9.Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships:tensorflow 10.DetNet: Design Backbone for Object Detection:pytorch 11.Receptive Field Block Net for Accurate and Fast Object Detection:pytorch
http://bbs.cvmart.net/topics/302/cvpr2019paper#1 CVPR2019 目标检测论文汇总 1.Bi-Directional Cascade Network for Perceptual Edge Detection：pytorch 2.Region Proposal by Guided Anchoring：pytorch 3.Learning Attraction Field Representation for Robust Line Segment Detection：pytorch 4.Latent Space Autoregression for Novelty Detection：Pytorch 5.Bounding Box Regression with Uncertainty for Accurate Object Detection（目标检测边界框回归损失算法）：Caffe2 and Detectron 6.Towards Universal Object Detection by Domain Attention： 7.A Simple Pooling-Based Design for Real-Time Salient Object Detection：pytorch 8.CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection：keras，tensorflow 9.ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch(Oral)：可能是pytorch 10.Pyramid Feature Attention Network for Saliency detection：tensorflow，keras 11.PPGNet: Learning Point-Pair Graph for Line Segment Detection：
