1、Stereo R-CNN based 3D Object Detection for Autonomous Driving
作者：Peiliang Li, Xiaozhi Chen, Shaojie Shen
论文链接：https://arxiv.org/abs/1902.09738
解读：Stereo 3D Object Detection - 知乎
2、Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
作者：Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, Silvio Savarese
论文链接：https://arxiv.org/abs/1902.09630
论文解读：CVPR2019 | 目标检测新文：Generalized Intersection over Union
3、ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape 作者：Fabian Manhardt, Wadim Kehl, Adrien Gaidon
论文链接：https://arxiv.org/abs/1812.02781
4、Bi-Directional Cascade Network for Perceptual Edge Detection
作者：Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang
论文链接：https://arxiv.org/abs/1902.10903
Github源码：https://github.com/pkuCactus/BDCN
5、RepMet: Representative-based metric learning for classification and one-shot object detection
作者：Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein
论文链接：https://arxiv.org/abs/1806.04728
6、Region Proposal by Guided Anchoring
作者：Jiaqi Wang, Kai Chen, Shuo Yang, Chen Change Loy, Dahua Lin
论文链接：https://arxiv.org/abs/1901.03278
论文解读：港中大-商汤联合实验室等提出：Guided Anchoring: 物体检测器也能自己学 Anchor
Github链接：GitHub - open-mmlab/mmdetection: OpenMMLab Detection Toolbox and Benchmark
7、Less is More: Learning Highlight Detection from Video Duration
作者：Bo Xiong, Yannis Kalantidis, Deepti Ghadiyaram, Kristen Grauman
论文链接：https://arxiv.org/abs/1903.00859
8、AIRD: Adversarial Learning Framework for Image Repurposing Detection
作者：Ayush Jaiswal, Yue Wu, Wael AbdAlmageed, Iacopo Masi, Premkumar Natarajan
论文链接：https://arxiv.org/abs/1903.00788
9、Feature Selective Anchor-Free Module for Single-Shot Object Detection
作者：Chenchen Zhu, Yihui He, Marios Savvides
论文链接：https://arxiv.org/abs/1903.00621
论文解读：CVPR2019 | CMU提出Single-Shot目标检测最强算法：FSAF
一作直播：CVPR2019 专题直播 | CMU 诸宸辰:基于 Anchor-free 特征选择模块的单阶目标检测
10、Learning Attraction Field Representation for Robust Line Segment Detection
作者：Nan Xue, Song Bai, Fudong Wang, Gui-Song Xia, Tianfu Wu, Liangpei Zhang
论文链接：https://arxiv.org/abs/1812.02122
代码链接：https://github.com/cherubicXN/afm_cvpr2019
11、Latent Space Autoregression for Novelty Detection
作者：Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara
论文链接：https://arxiv.org/abs/1807.01653
代码链接: GitHub - aimagelab/novelty-detection: Latent space autoregression for novelty detection.
12、Strong-Weak Distribution Alignment for Adaptive Object Detection
作者：Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko
论文链接：https://arxiv.org/abs/1812.04798
13、Few-shot Adaptive Faster R-CNN
作者：Tao Wang, Xiaopeng Zhang, Li Yuan, Jiashi Feng
论文链接：https://arxiv.org/abs/1903.09372
14、Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
作者：Liu Li, Mai Xu, Xiaofei Wang, Lai Jiang, Hanruo Liu
论文链接：https://arxiv.org/abs/1903.10831
15、Bounding Box Regression with Uncertainty for Accurate Object Detection（目标检测边界框回归损失算法）
作者：Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, Xiangyu Zhang
论文链接：https://arxiv.org/abs/1809.08545
代码链接：https://github.com/yihui-he/KL-Loss
解读：CMU和旷视科技开源：KL-Loss目标检测边界框回归新算法（CVPR2019）
16、Precise Detection in Densely Packed Scenes
作者：Eran Goldman , Roei Herzig, Aviv Eisenschtat, Jacob Goldberger, Tal Hassner
论文链接：https://arxiv.org/abs/1904.00853
17、Activity Driven Weakly Supervised Object Detection
作者：Zhenheng Yang, Dhruv Mahajan, Deepti Ghadiyaram, Ram Nevatia, Vignesh Ramanathan
论文链接：https://arxiv.org/pdf/1904.01665.pdf
18、Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
作者：Jason Ku, Alex D. Pon, Steven L. Waslander
论文链接：https://arxiv.org/pdf/1904.01690.pdf
19、Libra R-CNN: Towards Balanced Learning for Object Detection
作者：Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, Dahua Lin
论文链接：https://arxiv.org/abs/1904.02701
解读：浙大和商汤等提出：Libra RCNN目标检测新算法（特征融合），CVPR2019
20、Moving Object Detection under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition
作者：Moein Shakeri, Hong Zhang
论文链接：https://arxiv.org/abs/1904.03175
21、Towards Universal Object Detection by Domain Attention
作者：Xudong Wang, Zhaowei Cai, Dashan Gao, Nuno Vasconcelos
论文链接：https://arxiv.org/abs/1904.04402
项目链接：Universal Object Detection Benchmark
22、NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
作者：Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le
论文链接：https://arxiv.org/abs/1904.07392
23、Deep Anomaly Detection for Generalized Face Anti-Spoofing
作者：Daniel Pérez-Cabo, David Jiménez-Cabello, Artur Costa-Pazo, Roberto J. López-Sastre
论文链接：https://arxiv.org/abs/1904.08241
24、Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
作者：Zhe Wu, Li Su, Qingming Huang
论文链接：https://arxiv.org/abs/1904.08739
25、A Simple Pooling-Based Design for Real-Time Salient Object Detection
作者：Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, Jianmin Jiang
论文链接：https://arxiv.org/abs/1904.09569
源码链接：PoolNet+: Exploring the Potential of Pooling for Salient Object Detection – 程明明个人主页
26、CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection
作者：Lu Zhang; Huchuan Lu ; Zhe Lin ; Jianming Zhang; You He
论文链接：https://drive.google.com/open?id=1JcZMHBXEX-7AR1P010OXg_wCCC5HukeZ （需要申请）
源码链接：GitHub - zhangludl/code-and-dataset-for-CapSal: This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection', CVPR 2019.
27、Deep Fitting Degree Scoring Network for Monocular 3D Object Detection
作者：Lijie Liu1, Jiwen Lu, Chunjing Xu, Qi Tian, Jie Zhou
论文链接：https://arxiv.org/pdf/1904.12681.pdf
28、A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision
作者：Runmin Wu, Mengyang Feng, Wenlong Guan, Dong Wang, Huchuan Lu, Errui Ding
论文链接：待定
源码链接：https://github.com/JosephineRabbit/MLMSNet
29、ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch(Oral)
作者：Rui Zhu, Shifeng Zhang, Xiaobo Wang, Longyin Wen, Hailin Shi, Liefeng Bo, Tao Mei
论文链接：https://arxiv.org/abs/1810.08425v3
源码链接：GitHub - KimSoybean/ScratchDet: The code and models for paper: "ScratchDet: Exploring to Train Single-Shot Object Detectors from Scratch"