【资源】最全目标检测论文汇总(含最新 2019)
source link: https://bbs.cvmart.net/articles/1274
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
object-detection
[TOC]
This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to Date.
- R-CNN
- Fast R-CNN
- Faster R-CNN
- Mask R-CNN
- Light-Head R-CNN
- Cascade R-CNN
- SPP-Net
- YOLOv2
- YOLOv3
- MDSSD
- Pelee
- Fire SSD
- R-FCN
- RetinaNet
- MegDet
- RefineNet
- DetNet
- CornerNet
- M2Det
- 3D Object Detection
- ZSD(Zero-Shot Object Detection)
- OSD(One-Shot object Detection)
- Weakly Supervised Object Detection
- Softer-NMS
- Other
Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Survey
Imbalance Problems in Object Detection: A Review
- intro: under review at TPAMI
- arXiv: https://arxiv.org/abs/1909.00169
Recent Advances in Deep Learning for Object Detection
- intro: From 2013 (OverFeat) to 2019 (DetNAS)
- arXiv: https://arxiv.org/abs/1908.03673
A Survey of Deep Learning-based Object Detection
-
intro:From Fast R-CNN to NAS-FPN
- arXiv:https://arxiv.org/abs/1907.09408
Object Detection in 20 Years: A Survey
- intro:This work has been submitted to the IEEE TPAMI for possible publication
- arXiv:https://arxiv.org/abs/1905.05055
《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》
-
intro: awesome
- arXiv: https://arxiv.org/abs/1809.03193
《Deep Learning for Generic Object Detection: A Survey》
- intro: Submitted to IJCV 2018
- arXiv: https://arxiv.org/abs/1809.02165
Papers&Codes
R-CNN
Rich feature hierarchies for accurate object detection and semantic segmentation
Fast R-CNN
Fast R-CNN
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Faster R-CNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- intro: NIPS 2015
- arxiv: https://arxiv.org/abs/1506.01497
- gitxiv: https://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
- slides: https://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
- github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
- github(Caffe): https://github.com/rbgirshick/py-faster-rcnn
- github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
- github(PyTorch--recommend): https://github.com//jwyang/faster-rcnn.pytorch
- github: https://github.com/mitmul/chainer-faster-rcnn
- github(Torch):: https://github.com/andreaskoepf/faster-rcnn.torch
- github(Torch):: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
- github(TensorFlow): https://github.com/smallcorgi/Faster-RCNN_TF
- github(TensorFlow): https://github.com/CharlesShang/TFFRCNN
- github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
- github(Keras): https://github.com/yhenon/keras-frcnn
- github: https://github.com/Eniac-Xie/faster-rcnn-resnet
- github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev
R-CNN minus R
- intro: BMVC 2015
- arxiv: https://arxiv.org/abs/1506.06981
Faster R-CNN in MXNet with distributed implementation and data parallelization
Contextual Priming and Feedback for Faster R-CNN
An Implementation of Faster RCNN with Study for Region Sampling
Interpretable R-CNN
- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
- arxiv: https://arxiv.org/abs/1711.05226
Domain Adaptive Faster R-CNN for Object Detection in the Wild
- intro: CVPR 2018. ETH Zurich & ESAT/PSI
- arxiv: https://arxiv.org/abs/1803.03243
Mask R-CNN
Light-Head R-CNN
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Cascade R-CNN
Cascade R-CNN: Delving into High Quality Object Detection
SPP-Net
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
- intro: PAMI 2016
- intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
- project page: https://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
- arxiv: https://arxiv.org/abs/1412.5661
Object Detectors Emerge in Deep Scene CNNs
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Object Detection Networks on Convolutional Feature Maps
- intro: TPAMI 2015
- keywords: NoC
- arxiv: https://arxiv.org/abs/1504.06066
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
DeepBox: Learning Objectness with Convolutional Networks
You Only Look Once: Unified, Real-Time Object Detection
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
Start Training YOLO with Our Own Data
- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: https://guanghan.info/blog/en/my-works/train-yolo/
- github: https://github.com/Guanghan/darknet
YOLO: Core ML versus MPSNNGraph
TensorFlow YOLO object detection on Android
- intro: Real-time object detection on Android using the YOLO network with TensorFlow
- github: https://github.com/natanielruiz/android-yolo
Computer Vision in iOS – Object Detection
YOLOv2
YOLO9000: Better, Faster, Stronger
darknet_scripts
- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
- github: https://github.com/Jumabek/darknet_scripts
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
LightNet: Bringing pjreddie's DarkNet out of the shadows
https://github.com//explosion/lightnet
YOLO v2 Bounding Box Tool
- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
- github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
- arxiv: https://arxiv.org/abs/1804.04606
Object detection at 200 Frames Per Second
- intro: faster than Tiny-Yolo-v2
- arxiv: https://arxiv.org/abs/1805.06361
Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras
- intro: YOLE--Object Detection in Neuromorphic Cameras
- arxiv:https://arxiv.org/abs/1805.07931
OmniDetector: With Neural Networks to Bounding Boxes
- intro: a person detector on n fish-eye images of indoor scenes(NIPS 2018)
- arxiv:https://arxiv.org/abs/1805.08503
- datasets:https://gitlab.com/omnidetector/omnidetector
YOLOv3
YOLOv3: An Incremental Improvement
You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
SSD: Single Shot MultiBox Detector
What's the diffience in performance between this new code you pushed and the previous code? #327
https://github.com/weiliu89/caffe/issues/327
DSSD : Deconvolutional Single Shot Detector
Enhancement of SSD by concatenating feature maps for object detection
- intro: rainbow SSD (R-SSD)
- arxiv: https://arxiv.org/abs/1705.09587
Context-aware Single-Shot Detector
- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
- arxiv: https://arxiv.org/abs/1707.08682
Feature-Fused SSD: Fast Detection for Small Objects
https://arxiv.org/abs/1709.05054
FSSD: Feature Fusion Single Shot Multibox Detector
https://arxiv.org/abs/1712.00960
Weaving Multi-scale Context for Single Shot Detector
- intro: WeaveNet
- keywords: fuse multi-scale information
- arxiv: https://arxiv.org/abs/1712.03149
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
https://arxiv.org/abs/1801.05918
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
https://arxiv.org/abs/1802.06488
MDSSD
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
Pelee
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Robert-JunWang/Pelee
Fire SSD
Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device
-
intro:low cost, fast speed and high mAP on factor edge computing devices
- arxiv:https://arxiv.org/abs/1806.05363
R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN-3000 at 30fps: Decoupling Detection and Classification
https://arxiv.org/abs/1712.01802
Recycle deep features for better object detection
Feature Pyramid Networks for Object Detection
- intro: Facebook AI Research
- arxiv: https://arxiv.org/abs/1612.03144
Action-Driven Object Detection with Top-Down Visual Attentions
Beyond Skip Connections: Top-Down Modulation for Object Detection
- intro: CMU & UC Berkeley & Google Research
- arxiv: https://arxiv.org/abs/1612.06851
Wide-Residual-Inception Networks for Real-time Object Detection
- intro: Inha University
- arxiv: https://arxiv.org/abs/1702.01243
Attentional Network for Visual Object Detection
- intro: University of Maryland & Mitsubishi Electric Research Laboratories
- arxiv: https://arxiv.org/abs/1702.01478
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
- keykwords: CC-Net
- intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
- arxiv: https://arxiv.org/abs/1702.07054
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
- intro: ICCV 2017 (poster)
- arxiv: https://arxiv.org/abs/1703.10295
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03944
Spatial Memory for Context Reasoning in Object Detection
Accurate Single Stage Detector Using Recurrent Rolling Convolution
- intro: CVPR 2017. SenseTime
- keywords: Recurrent Rolling Convolution (RRC)
- arxiv: https://arxiv.org/abs/1704.05776
- github: https://github.com/xiaohaoChen/rrc_detection
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
https://arxiv.org/abs/1704.05775
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
- intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
- arxiv: https://arxiv.org/abs/1705.05922
Point Linking Network for Object Detection
- intro: Point Linking Network (PLN)
- arxiv: https://arxiv.org/abs/1706.03646
Perceptual Generative Adversarial Networks for Small Object Detection
https://arxiv.org/abs/1706.05274
Few-shot Object Detection
https://arxiv.org/abs/1706.08249
Yes-Net: An effective Detector Based on Global Information
https://arxiv.org/abs/1706.09180
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
https://arxiv.org/abs/1706.10217
Towards lightweight convolutional neural networks for object detection
https://arxiv.org/abs/1707.01395
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Mimicking Very Efficient Network for Object Detection
- intro: CVPR 2017. SenseTime & Beihang University
- paper: https://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf
Residual Features and Unified Prediction Network for Single Stage Detection
https://arxiv.org/abs/1707.05031
Deformable Part-based Fully Convolutional Network for Object Detection
- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
- arxiv: https://arxiv.org/abs/1707.06175
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1707.06399
Recurrent Scale Approximation for Object Detection in CNN
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
- intro: BMVC 2018
- arXiv: https://arxiv.org/abs/1807.11013
Object Detection from Scratch with Deep Supervision
- intro: This is an extended version of DSOD
- arXiv: https://arxiv.org/abs/1809.09294
RetinaNet
Focal Loss for Dense Object Detection
- intro: ICCV 2017 Best student paper award. Facebook AI Research
- keywords: RetinaNet
- arxiv: https://arxiv.org/abs/1708.02002
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.02863
Incremental Learning of Object Detectors without Catastrophic Forgetting
- intro: ICCV 2017. Inria
- arxiv: https://arxiv.org/abs/1708.06977
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
https://arxiv.org/abs/1709.04347
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
https://arxiv.org/abs/1709.05788
Dynamic Zoom-in Network for Fast Object Detection in Large Images
https://arxiv.org/abs/1711.05187
Zero-Annotation Object Detection with Web Knowledge Transfer
- intro: NTU, Singapore & Amazon
- keywords: multi-instance multi-label domain adaption learning framework
- arxiv: https://arxiv.org/abs/1711.05954
MegDet
MegDet: A Large Mini-Batch Object Detector
- intro: Peking University & Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07240
Receptive Field Block Net for Accurate and Fast Object Detection
An Analysis of Scale Invariance in Object Detection - SNIP
Feature Selective Networks for Object Detection
https://arxiv.org/abs/1711.08879
Learning a Rotation Invariant Detector with Rotatable Bounding Box
Scalable Object Detection for Stylized Objects
- intro: Microsoft AI & Research Munich
- arxiv: https://arxiv.org/abs/1711.09822
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Deep Regionlets for Object Detection
- keywords: region selection network, gating network
- arxiv: https://arxiv.org/abs/1712.02408
Training and Testing Object Detectors with Virtual Images
- intro: IEEE/CAA Journal of Automatica Sinica
- arxiv: https://arxiv.org/abs/1712.08470
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
- keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
- arxiv: https://arxiv.org/abs/1712.08832
Spot the Difference by Object Detection
- intro: Tsinghua University & JD Group
- arxiv: https://arxiv.org/abs/1801.01051
Localization-Aware Active Learning for Object Detection
Object Detection with Mask-based Feature Encoding
LSTD: A Low-Shot Transfer Detector for Object Detection
- intro: AAAI 2018
- arxiv: https://arxiv.org/abs/1803.01529
Pseudo Mask Augmented Object Detection
https://arxiv.org/abs/1803.05858
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
https://arxiv.org/abs/1803.06799
Learning Region Features for Object Detection
- intro: Peking University & MSRA
- arxiv: https://arxiv.org/abs/1803.07066
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
- intro: Singapore Management University & Zhejiang University
- arxiv: https://arxiv.org/abs/1803.08208
Object Detection for Comics using Manga109 Annotations
- intro: University of Tokyo & National Institute of Informatics, Japan
- arxiv: https://arxiv.org/abs/1803.08670
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Transferring Common-Sense Knowledge for Object Detection
Multi-scale Location-aware Kernel Representation for Object Detection
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- intro: National University of Defense Technology
- arxiv: https://arxiv.org/abs/1804.04606
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
RefineNet
Single-Shot Refinement Neural Network for Object Detection
DetNet
DetNet: A Backbone network for Object Detection
- intro: Tsinghua University & Face++
- arxiv: https://arxiv.org/abs/1804.06215
Self-supervisory Signals for Object Discovery and Detection
- Google Brain
- arxiv:https://arxiv.org/abs/1806.03370
CornerNet
CornerNet: Detecting Objects as Paired Keypoints
M2Det
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
3D Object Detection
3D Backbone Network for 3D Object Detection
LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs
ZSD(Zero-Shot Object Detection)
Zero-Shot Detection
- intro: Australian National University
- keywords: YOLO
- arxiv: https://arxiv.org/abs/1803.07113
Zero-Shot Object Detection
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Zero-Shot Object Detection by Hybrid Region Embedding
OSD(One-Shot Object Detection)
Comparison Network for One-Shot Conditional Object Detection
One-Shot Object Detection
RepMet: Representative-based metric learning for classification and one-shot object detection
- intro: IBM Research AI
- arxiv:https://arxiv.org/abs/1806.04728
- github: TODO
Weakly Supervised Object Detection
Weakly Supervised Object Detection in Artworks
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Softer-NMS
《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》
Feature Selective Anchor-Free Module for Single-Shot Object Detection
-
intro: CVPR 2019
- arXiv: https://arxiv.org/abs/1903.00621
Object Detection based on Region Decomposition and Assembly
-
intro: AAAI 2019
- arXiv: https://arxiv.org/abs/1901.08225
Bottom-up Object Detection by Grouping Extreme and Center Points
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
-
intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- arXiv: https://arxiv.org/abs/1901.07925
Consistent Optimization for Single-Shot Object Detection
-
intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase
- arXiv: https://arxiv.org/abs/1901.06563
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
Region Proposal by Guided Anchoring
- intro: CUHK - SenseTime Joint Lab
- arXiv: https://arxiv.org/abs/1901.03278
Scale-Aware Trident Networks for Object Detection
- intro: mAP of 48.4 on the COCO dataset
- arXiv: https://arxiv.org/abs/1901.01892
Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions
Strong-Weak Distribution Alignment for Adaptive Object Detection
AutoFocus: Efficient Multi-Scale Inference
- intro: AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU
- arXiv: https://arxiv.org/abs/1812.01600
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
- intro: Google Could
- arXiv: https://arxiv.org/abs/1812.00124
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
- intro: UC Berkeley
- arXiv: https://arxiv.org/abs/1812.00929
Grid R-CNN
- intro: SenseTime
- arXiv: https://arxiv.org/abs/1811.12030
Deformable ConvNets v2: More Deformable, Better Results
-
intro: Microsoft Research Asia
- arXiv: https://arxiv.org/abs/1811.11168
Anchor Box Optimization for Object Detection
- intro: Microsoft Research
- arXiv: https://arxiv.org/abs/1812.00469
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection
- intro: Microsoft Research Asia
- arXiv: https://arxiv.org/abs/1811.11167
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
CFENet: Object Detection with Comprehensive Feature Enhancement Module
- intro: ACCV 2018
- github: https://github.com/qijiezhao/CFENet
DeRPN: Taking a further step toward more general object detection
Hybrid Knowledge Routed Modules for Large-scale Object Detection
《Receptive Field Block Net for Accurate and Fast Object Detection》
Deep Feature Pyramid Reconfiguration for Object Detection
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.07993
Unsupervised Hard Example Mining from Videos for Improved Object Detection
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.04285
Acquisition of Localization Confidence for Accurate Object Detection
Toward Scale-Invariance and Position-Sensitive Region Proposal Networks
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1807.09528
MetaAnchor: Learning to Detect Objects with Customized Anchors
Relation Network for Object Detection
Quantization Mimic: Towards Very Tiny CNN for Object Detection
- Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
- arxiv: https://arxiv.org/abs/1805.02152
Learning Rich Features for Image Manipulation Detection
- intro: CVPR 2018 Camera Ready
- arxiv: https://arxiv.org/abs/1805.04953
SNIPER: Efficient Multi-Scale Training
Soft Sampling for Robust Object Detection
- intro: the robustness of object detection under the presence of missing annotations
- arxiv:https://arxiv.org/abs/1806.06986
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
Other
R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
Detection Toolbox
- Detectron(FAIR): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.
- Detectron2: Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
- maskrcnn-benchmark(FAIR): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
- mmdetection(SenseTime&CUHK): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.
来源:awesome-object-detection@Github
作者:amusi
更多Awsome Github资源请关注:【Awsome】GitHub 资源汇总
推荐阅读:
CVPR2018 目标检测(object detection)算法总览
CVPR 2019 论文大盘点—目标检测篇
【框架】PyTorch 图像检索框架
△ 扫一扫关注 极市平台
每天推送最新CV干货
微信公众号: 极市平台(ID: extrememart )
每天推送最新CV干货
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK