目标检测(物体检测, Object Detection) 专知荟萃
入门学习
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图像目标检测(Object Detection)原理与实现 (1-6)
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. 基于特征共享的高效物体检测 Faster R-CNN和ResNet的作者任少卿 博士毕业论文 中文
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R-CNN:论文笔记
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Fast-RCNN:
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Faster-RCNN:
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FPN:
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R-FCN:
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SSD:
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YOLO:
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DenseBox:余凯特邀报告:基于密集预测图的物体检测技术造就全球领先的ADAS系统
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- [http://www.cnblogs.com/xueyuxiaolang/p/5959442.html]
深度学习论文笔记:DSSD
- [http://jacobkong.github.io/posts/2938514597/]
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DSOD
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Focal Loss:
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Soft-NMS:
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OHEM:
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Mask-RCNN 2017:
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目标检测之比较
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视觉目标检测和识别之过去,现在及可能
进阶文章
- Deep Neural Networks for Object Detection (基于DNN的对象检测)NIPS2013:
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R-CNN Rich feature hierarchies for accurate object detection and semantic segmentation:
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Fast R-CNN :
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Faster R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks:
- Scalable Object Detection using Deep Neural Networks
- Scalable, High-Quality Object Detection
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SPP-Net Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
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DeepID-Net DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
- 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
- Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
- DeepBox: Learning Objectness with Convolutional Networks
- Object detection via a multi-region & semantic segmentation-aware CNN model
- You Only Look Once: Unified, Real-Time Object Detection
- YOLOv2 YOLO9000: Better, Faster, Stronger
- AttentionNet: Aggregating Weak Directions for Accurate Object Detection
- DenseBox: Unifying Landmark Localization with End to End Object Detection
- SSD: Single Shot MultiBox Detector
- DSSD : Deconvolutional Single Shot Detector
- G-CNN: an Iterative Grid Based Object Detector
- HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
- A MultiPath Network for Object Detection
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
- PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- Feature Pyramid Networks for Object Detection
- Learning Chained Deep Features and Classifiers for Cascade in Object Detection
- DSOD: Learning Deeply Supervised Object Detectors from Scratch
- Focal Loss for Dense Object Detection ICCV 2017 Best student paper award. Facebook AI Research
综述
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深度学习之 "物体检测" 方法梳理
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地平线黄李超开讲:深度学习和物体检测!:
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对话CVPR2016:目标检测新进展:
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基于深度学习的目标检测技术演进:R-CNN、Fast R-CNN、Faster R-CNN:
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基于深度学习的目标检测研究进展
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讲堂干货No.1|山世光-基于深度学习的目标检测技术进展与展望
Tutorial
- CVPR'17 Tutorial Deep Learning for Objects and Scenes by Kaiming He Ross Girshick
- ICCV 2015 Tools for Efficient Object Detection
- Object Detection
- Image Recognition and Object Detection : Part 1
- R-CNN for Object Detection
视频教程
- cs231 第11讲 Detection and Segmentation
- Deep Learning for Instance-level Object Understanding
by Ross Girshick.
代码
- R-CNN
- Fast R-CNN:
- Faster R-CNN
- SPP-Net
- YOLO
- YOLOv2
- SSD
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Recurrent Scale Approximation for Object Detection in CNN
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Mask-RCNN 2017
领域专家
- Ross Girshick (rbg 大神)
- Kaiming He, Facebook人工智能实验室科学家Kaiming He
- Shaoqing Ren
- Jian Sun
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