文章目錄
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- [CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
- anchor-free
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- [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
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- [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
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- [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
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- [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
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[CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
- arxiv: https://arxiv.org/abs/1902.09630
anchor-free
無錨框最近的熱點,有機會研究下。
[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改進
- arxiv: https://arxiv.org/abs/1904.08189
- github: https://github.com/Duankaiwen/CenterNet
[CVPR2019]Objects as Points
- intro: CornerNet改進
- arxiv: https://arxiv.org/pdf/1904.07850.pdf
- github: https://github.com/xingyizhou/CenterNet
[CVPR2019]CornerNet-Lite: Efficient Keypoint Based Object Detection
- intro: CornerNet改進,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