Object Detection(目标检测神文)(二)

文章目录

• [CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
• anchor-free
• [CVPR2019] Region Proposal by Guided Anchoring
• [CVPR2019] Feature Selective Anchor-Free Module for Single-Shot Object Detection
• [CVPR2019]CenterNet: Keypoint Triplets for Object Detection
• [CVPR2019]Objects as Points
• [CVPR2019]CornerNet-Lite: Efficient Keypoint Based Object Detection
• [CVPR2019]FoveaBox: Beyond Anchor-based Object Detector
• [2019]DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors
• YOLO
• [2019]Spiking-YOLO: Spiking Neural Network for Real-time Object Detection
• [CVPR2019]Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
• [AAAI2019]Gradient Harmonized Single-stage Detector
• [2019]Augmentation for small object detection
• [2019]SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
• [2019]BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
• [2019]DetNAS: Neural Architecture Search on Object Detection
• [2019]ThunderNet: Towards Real-time Generic Object Detection
• [2019]Feature Intertwiner for Object Detection
• [CVPR2019]Few-shot Adaptive Faster R-CNN
• [2019]Improving Object Detection with Inverted Attention
• [2019]FCOS: Fully Convolutional One-Stage Object Detection
• [CVPR2019]Libra R-CNN: Towards Balanced Learning for Object Detection
• [2019]Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
• [CVPR2019]What Object Should I Use? - Task Driven Object Detection
• [CVPR2019]Towards Universal Object Detection by Domain Attention
• [2019]Prime Sample Attention in Object Detection
• [2019]BAOD: Budget-Aware Object Detection
• [2019]An Analysis of Pre-Training on Object Detection
• [2019]Rethinking Classification and Localization in R-CNN
• [CVPR2019]NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
• [2019]Automated Focal Loss for Image based Object Detection
• [2019]LFFD: A Light and Fast Face Detector for Edge Devices
• [CVPR2019]Exploring Object Relation in Mean Teacher for Cross-Domain Detection
• [2019]HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection
• [2019]An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
• [2019]RepPoints: Point Set Representation for Object Detection
• [2019]Object Detection in 20 Years: A Survey
• [AAAI2019]SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
• [2019]Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
• [2019]Progressive Sparse Local Attention for Video object detection
• [2019]Exploring the Semantics for Visual Relationship Detection
• [2019]PyramidBox++: High Performance Detector for Finding Tiny Face
• [ICPR2018]MSFD:Multi-Scale Receptive Field Face Detector
• [CVPR2018]Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors
• [ECCV2018]Bi-box Regression for Pedestrian Detection and Occlusion Estimation
• [2019]SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection
• [2019]Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
• [2019]GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection
• [CVPR2019]High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection
• Pedestrian Detection in a Crowd
• [CVPR2018]Repulsion Loss: Detecting Pedestrians in a Crowd
• [ECCV2018]Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
• [CVPR2019]Adaptive NMS: Refining Pedestrian Detection in a Crowd
• [2019]Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

[CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

• arxiv: https://arxiv.org/abs/1902.09630

anchor-free

无锚框最近的热点，有机会研究下。

[CVPR2019] Region Proposal by Guided Anchoring

• intro: CUHK - SenseTime Joint Lab & Amazon Rekognition & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1901.03278

[CVPR2019] Feature Selective Anchor-Free Module for Single-Shot Object Detection

• intro: FSAF for Single-Shot Object Detection
• arxiv: https://arxiv.org/abs/1903.00621

[CVPR2019]CenterNet: Keypoint Triplets for Object Detection

• intro: CornerNet改进
• arxiv: https://arxiv.org/abs/1904.08189
• github: https://github.com/Duankaiwen/CenterNet

[CVPR2019]Objects as Points

• intro: CornerNet改进
• arxiv: https://arxiv.org/pdf/1904.07850.pdf
• github: https://github.com/xingyizhou/CenterNet

[CVPR2019]CornerNet-Lite: Efficient Keypoint Based Object Detection

• intro: CornerNet改进，mAP34.4%-34ms
• arxiv: https://arxiv.org/abs/1904.08900
• github: https://github.com/princeton-vl/CornerNet-Lite

[CVPR2019]FoveaBox: Beyond Anchor-based Object Detector

• intro:
• arxiv: https://arxiv.org/abs/1904.03797
• github:

[2019]DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors

• intro: Baidu Inc.
• arxiv: https://arxiv.org/abs/1904.06883

YOLO

[2019]Spiking-YOLO: Spiking Neural Network for Real-time Object Detection

• arxiv: https://arxiv.org/abs/1903.06530

[CVPR2019]Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

• arxiv: https://arxiv.org/abs/1904.04620

[AAAI2019]Gradient Harmonized Single-stage Detector

• intro: AAAI 2019 Oral
• arxiv: https://arxiv.org/abs/1811.05181
• gihtub(official): https://github.com/libuyu/GHM_Detection

[2019]Augmentation for small object detection

• arxiv: https://arxiv.org/abs/1902.07296

[2019]SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition

• intro: TuSimple
• arxiv: https://arxiv.org/abs/1903.05831
• github: https://github.com/tusimple/simpledet

[2019]BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

• intro: University of Toronto
• arxiv: https://arxiv.org/abs/1903.03838

[2019]DetNAS: Neural Architecture Search on Object Detection

• intro: Chinese Academy of Sciences & Megvii Inc
• arxiv: https://arxiv.org/abs/1903.10979

[2019]ThunderNet: Towards Real-time Generic Object Detection

https://arxiv.org/abs/1903.11752

[2019]Feature Intertwiner for Object Detection

• intro: ICLR 2019
• intro: CUHK & SenseTime & The University of Sydney
• arxiv: https://arxiv.org/abs/1903.11851

[CVPR2019]Few-shot Adaptive Faster R-CNN

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1903.09372

[2019]Improving Object Detection with Inverted Attention

• arxiv: https://arxiv.org/abs/1903.12255

[2019]FCOS: Fully Convolutional One-Stage Object Detection

• arxiv: https://arxiv.org/abs/1904.01355

[CVPR2019]Libra R-CNN: Towards Balanced Learning for Object Detection

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1904.02701

[2019]Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

• arxiv: https://arxiv.org/abs/1904.07537

[CVPR2019]What Object Should I Use? - Task Driven Object Detection

intro: CVPR 2019
arxiv: https://arxiv.org/abs/1904.03000

FoveaBox: Beyond Anchor-based Object Detector
intro: Tsinghua University & BNRist & ByteDance AI Lab & University of Pennsylvania
arxiv: https://arxiv.org/abs/1904.03797

[CVPR2019]Towards Universal Object Detection by Domain Attention

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1904.04402

[2019]Prime Sample Attention in Object Detection

• arxiv: https://arxiv.org/abs/1904.04821

[2019]BAOD: Budget-Aware Object Detection

• arxiv: https://arxiv.org/abs/1904.05443

[2019]An Analysis of Pre-Training on Object Detection

• intro: University of Maryland
• arxiv: https://arxiv.org/abs/1904.05871

[2019]Rethinking Classification and Localization in R-CNN

• intro: Northeastern University & Microsoft
• arxiv: https://arxiv.org/abs/1904.06493

[CVPR2019]NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

• intro: CVPR 2019, Google Brain
• arxiv: https://arxiv.org/abs/1904.07392

[2019]Automated Focal Loss for Image based Object Detection

• arxiv: https://arxiv.org/abs/1904.09048

[2019]LFFD: A Light and Fast Face Detector for Edge Devices

• arxiv: https://arxiv.org/abs/1904.10633

[CVPR2019]Exploring Object Relation in Mean Teacher for Cross-Domain Detection

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1904.11245

[2019]HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection

• arxiv: https://arxiv.org/abs/1904.11141

[2019]An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

intro: CVPR 2019 CEFRL Workshop
arxiv: https://arxiv.org/abs/1904.09730

[2019]RepPoints: Point Set Representation for Object Detection

• intro: Peking University & Tsinghua University & Microsoft Research Asia
• arxiv: https://arxiv.org/abs/1904.11490

[2019]Object Detection in 20 Years: A Survey

• arxiv: https://arxiv.org/abs/1905.05055

[AAAI2019]SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection

• intro: AAAI 2019
• arxiv: https://arxiv.org/abs/1903.07663

[2019]Looking Fast and Slow: Memory-Guided Mobile Video Object Detection

• intro: Cornell University & Google AI
• arxiv: https://arxiv.org/abs/1903.10172

[2019]Progressive Sparse Local Attention for Video object detection

• intro: NLPR,CASIA & Horizon Robotics
• arxiv: https://arxiv.org/abs/1903.09126

[2019]Exploring the Semantics for Visual Relationship Detection

• arxiv: https://arxiv.org/abs/1904.02104

[2019]PyramidBox++: High Performance Detector for Finding Tiny Face

• intro: Chinese Academy of Sciences & Baidu, Inc.
• arxiv: https://arxiv.org/abs/1904.00386

[ICPR2018]MSFD:Multi-Scale Receptive Field Face Detector

• intro: ICPR 2018
• arxiv: https://arxiv.org/abs/1903.04147

[CVPR2018]Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors

• intro: CVPR 2018
• paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Noh_Improving_Occlusion_and_CVPR_2018_paper.pdf

[ECCV2018]Bi-box Regression for Pedestrian Detection and Occlusion Estimation

• intro: ECCV 2018
• paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf
• github(Pytorch): https://github.com/rainofmine/Bi-box_Regression

[2019]SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1902.09080

[2019]Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

• arxiv: https://arxiv.org/abs/1902.05291

[2019]GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection

• arxiv: https://arxiv.org/abs/1903.06999

[CVPR2019]High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection

• intro: CVPR 2019
• intro: National University of Defense Technology & Chinese Academy of Sciences & Inception Institute of Artificial Intelligence (IIAI) & Horizon Robotics Inc.
• arxiv: https://arxiv.org/abs/1904.02948
• github(official, Keras): https://github.com/liuwei16/CSP

Pedestrian Detection in a Crowd

[CVPR2018]Repulsion Loss: Detecting Pedestrians in a Crowd

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1711.07752

[ECCV2018]Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1807.08407

[CVPR2019]Adaptive NMS: Refining Pedestrian Detection in a Crowd

• intro: CVPR 2019 oral
• arxiv: https://arxiv.org/abs/1904.03629

[2019]Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

• arxiv: https://arxiv.org/abs/1904.03692