计算机视觉论文笔记

写在前面

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论文笔记:

一、图像分类

  1. ZFNet(Visualizing and Understanding Convolutional Networks)
  2. GoogLeNet(Inception v1)
  3. Inception v2/v3
  4. ResNet(Deep Residual Learning for Image Recognition)
  5. Inception V4 && Inception ResNet
  6. MobileNet V1(MobileNets:Efficient convolutional neural networks for mobile vision applications)
  7. Xception(Xception: Deep Learning With Depthwise Separable Convolutions)
  8. MobileNet V2(MobileNetV2: Inverted Residuals and Linear Bottlenecks)
  9. MnasNet(MnasNet: Platform-Aware Neural Architecture Search for Mobile)
  10. MobileNet V3(Searching for MobileNetV3)
  11. ShuffleNet V1(ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices)
  12. ShuffleNet V2(ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design)

二、目标检测

2.1自然场景中的目标检测

  1. YOLO v1(You Only Look Once: Unified, Real-Time Object Detection)
  2. YOLO v2(YOLO9000: Better, Faster, Stronger)
  3. YOLO v3(YOLOv3: An Incremental Improvement)
  4. YOLO v4
  5. YOLOX(YOLOX: Exceeding YOLO Series in 2021)
  6. RCNN(Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation)
  7. SPP-net(Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition)
  8. Fast R-CNN(Fast R-CNN)
  9. Faster R-CNN(Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)
  10. FPN(Feature Pyramid Networks for Object Detection)
  11. RetinaNet(Focal Loss for Dense Object Detection)
  12. CornerNet: Detecting Objects as Paired Keypoints
  13. CenterNet:Objects as points
  14. SSD: Single Shot MultiBox Detector

2.2 遥感图像目标检测

  1. R2CNN-NOTA(R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy)
  2. RoI Transformer(Learning RoI Transformer for Oriented Object Detection in Aerial Images)
  3. RRPN:Rotated_text_dectection(Arbitrary-Oriented Scene Text Detection via Rotation Proposals)
  4. DOTA(DOTA: A Large-scale Dataset for Object DeTection in Aerial Images - Dataset)
  5. R³Det(R³Det:Refined Single-Stage Detector with Feature Refifinement for Rotating Object)
  6. S²A-Net(Align Deep Features for Oriented Object Detectio)
  7. DMNet:Density Map Guided Object Detection in Aerial Images
  8. DAL:Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
  9. ClusDet:Clustered Object Detection in Aerial Images
  10. BBAVector:Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
  11. ReDet: A Rotation-equivariant Detector for Aerial Object Detection

2.3 小物体目标检测

  • 小目标(small): a r e a < 3 2 2 area<32^2 area<322
  • 中目标(medium): 3 2 2 < a r e a < 9 6 2 32^2322<area<962
  • 大目标(large): 9 6 2 < a r e a 96^2962<area
  1. QueryDet:Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

三、图像分割

3.1 传统方法

  1. 基于边缘的图像分割方法
  2. 基于阈值的图像分割
  3. 基于区域的图像分割

3.2 语义分割

  1. FCN(Fully Convolutional Networks for Semantic Segmentation)
  2. U-Net: Convolutional Networks for Biomedical Image Segmentation

3.3 实例分割

暂无

四、目标跟踪

4.1 单摄像头单目标跟踪

  1. 视频单目标跟踪研究进展综述

4.2 单摄像头多目标跟踪

  1. FairMot:On the Fairness of Detection and Re-Identification in Multiple Object Tracking
  2. TraDeS:Track to Detect and Segment: An Online Multi-Object Tracker
  3. QDTrack:Quasi-Dense Similarity Learning for Multiple Object Tracking
  4. CenterTrack:Tracking Objects as Points
  5. Multiple Object Tracking: A Literature Review
  6. Persistent Tracking for Wide Area Aerial Surveillance
  7. MeMot : Multi-Object Tracking with Memory

4.3 多摄像头多目标跟踪

  1. Branch-and-price global optimization for multi-view multi-target tracking
  2. Hypergraphs for Joint Multi-View Reconstruction and Multi-Object Tracking
  3. Multi-Camera Multi-Target Tracking with Space-Time-View Hyper-graph
  4. Vehicle Re-Identification with the Space-Time Prior
  5. Features for Multi-Target Multi-Camera Tracking and Re-Identification
  6. Real-Time Multi-Target Multi-Camera Tracking with Spatial-Temporal Information
  7. CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
  8. People tracking in multi-camera systems: a review
  9. Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
  10. The MTA Dataset for Multi-Target Multi-Camera Pedestrian Tracking by Weighted Distance Aggregation
  11. Pose-Assisted Multi-Camera Collaboration System
  12. ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City
  13. Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models
  14. TRACTA
  15. City-Scale Multi-Camera Vehicle Tracking by Semantic Attribute Parsing and Cross-Camera Tracklet Matching
  16. Real-time 3D Deep Multi-Camera Tracking

4.4 Re-Identification

  1. In Defense of the Triplet Loss for Person Re-Identification

五、计数

5.1 基于密度估计的计数方法

  1. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
  2. 基于深度卷积神经网络的田间麦穗密度估计及计数

5.2 基于目标检测的计数方法

  1. Vehicle counting and traffic flow parameter estimation for dense traffic scenes
  2. 基于改进实例分割算法的智能猪只盘点系统设计
  3. A machine vision system to detect and count laying hens

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