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目标检测论文集

2017-03-22

Papers

Object Detection

  • R-CNN: Rich feature hierarchies for accurate object detection and semantic segmentation.【链接】
  • SPP-Net: Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.【链接】
  • Fast R-CNN: Fast R-CNN.【链接】
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.【链接】
  • Mask R-CNN.【链接】
  • MultiBox: Scalable Object Detection using Deep Neural Networks.【链接】
  • DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.【链接】
  • NoC: Object Detection Networks on Convolutional Feature Maps.【链接】
  • DeepBox: Learning Objectness with Convolutional Networks.【链接】
  • MR-CNN: Object detection via a multi-region & semantic segmentation-aware CNN model.【链接】
  • YOLO: You Only Look Once: Unified, Real-Time Object Detection.【链接】
  • YOLOv2: YOLO9000: Better, Faster, Stronger.【链接】
  • AttentionNet: Aggregating Weak Directions for Accurate Object Detection.【链接】
  • DenseBox: Unifying Landmark Localization with End to End Object Detection.【链接】
  • SSD: Single Shot MultiBox Detector.【链接】
  • DSSD: Deconvolutional Single Shot Detector.【链接】
  • Inside-Outside Net(ION): Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks.【链接】
  • G-CNN: An Iterative Grid Based Object Detector.【链接】
  • HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection.【链接】
  • MultiPathNet: A MultiPath Network for Object Detection.【链接】
  • CRAFT: CRAFT Objects from Images.【链接】
  • OHEM: Training Region-based Object Detectors with Online Hard Example Mining.【链接】
  • R-FCN: Object Detection via Region-based Fully Convolutional Networks.【链接】
  • MS-CNN: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection.【链接】
  • PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection.【链接】
  • GBD-Net: Gated Bi-directional CNN for Object Detection.【链接】
  • StuffNet: Using ‘Stuff’ to Improve Object Detection.【链接】
  • Feature Pyramid Network(FPN): Feature Pyramid Networks for Object Detection.【链接】
  • CC-Net: Learning Chained Deep Features and Classifiers for Cascade in Object Detection.【链接】
  • DSOD: Learning Deeply Supervised Object Detectors from Scratch.【链接】
  • NMS: End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression.【链接】
  • Weakly Supervised Object Detection: Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection.【链接】

Object Detection in 3D

  • Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks.【链接】

Object Detection on RGB-D

  • Learning Rich Features from RGB-D Images for Object Detection and Segmentation.【链接】
  • Differential Geometry Boosts Convolutional Neural Networks for Object Detection.【链接】
  • A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation.【链接】

Salient Object Detection

  • Best Deep Saliency Detection Models (CVPR 2016 & 2015).【链接】
  • Large-scale optimization of hierarchical features for saliency prediction in natural images.【链接】
  • Predicting Eye Fixations using Convolutional Neural Networks.【链接】
  • Saliency Detection by Multi-Context Deep Learning.【链接】
  • DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection.【链接】
  • SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection.【链接】
  • Shallow and Deep Convolutional Networks for Saliency Prediction.【链接】
  • Recurrent Attentional Networks for Saliency Detection.【链接】
  • Two-Stream Convolutional Networks for Dynamic Saliency Prediction.【链接】
  • Unconstrained Salient Object Detection via Proposal Subset Optimization.【链接】
  • DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection.【链接】
  • Salient Object Subitizing.【链接】
  • Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection.【链接】
  • Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs.【链接】
  • Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection.【链接】
  • A Deep Multi-Level Network for Saliency Prediction.【链接】
  • Visual Saliency Detection Based on Multiscale Deep CNN Features.【链接】
  • A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection.【链接】
  • Deeply supervised salient object detection with short connections.【链接】
  • Weakly Supervised Top-down Salient Object Detection.【链接】
  • SalGAN: Visual Saliency Prediction with Generative Adversarial Networks.【链接】
  • Visual Saliency Prediction Using a Mixture of Deep Neural Networks.【链接】
  • A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network.【链接】
  • Saliency Detection by Forward and Backward Cues in Deep-CNNs.【链接】
  • Supervised Adversarial Networks for Image Saliency Detection.【链接】
  • Group-wise Deep Co-saliency Detection.【链接】
  • Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection.【链接】
  • Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection.【链接】
  • Learning Uncertain Convolutional Features for Accurate Saliency Detection.【链接】
  • Deep Edge-Aware Saliency Detection.【链接】
  • Self-explanatory Deep Salient Object Detection.【链接】
  • PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection.【链接】
  • DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets.【链接】

Saliency Detection in Video

  • Deep Learning For Video Saliency Detection.【链接】
  • Video Salient Object Detection Using Spatiotemporal Deep Features.【链接】
  • Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM.【链接】

Visual Relationship Detection

  • Visual Relationship Detection with Language Priors.【链接】
  • ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection.【链接】
  • Visual Translation Embedding Network for Visual Relation Detection.【链接】
  • Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection.【链接】
  • Detecting Visual Relationships with Deep Relational Networks.【链接】
  • Identifying Spatial Relations in Images using Convolutional Neural Networks.【链接】
  • PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN.[【链接】][【链接】]【链接】

Specific Object Deteciton

  • Deep Deformation Network for Object Landmark Localization.[【链接】]【链接】
  • Fashion Landmark Detection in the Wild.【链接】
  • Deep Learning for Fast and Accurate Fashion Item Detection.[【链接】]【链接】
  • OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”).【链接】
  • Selfie Detection by Synergy-Constraint Based Convolutional Neural Network.【链接】
  • Associative Embedding:End-to-End Learning for Joint Detection and Grouping.【链接】
  • Deep Cuboid Detection: Beyond 2D Bounding Boxes.【链接】
  • Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection.【链接】
  • Deep Learning Logo Detection with Data Expansion by Synthesising Context.【链接】
  • Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks.【链接】
  • Automatic Handgun Detection Alarm in Videos Using Deep Learning.【链接】
  • Using Deep Networks for Drone Detection.【链接】
  • Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection.【链接】
  • DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion.【链接】
  • Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network.【链接】

Face Deteciton

  • Multi-view Face Detection Using Deep Convolutional Neural Networks.【链接】
  • From Facial Parts Responses to Face Detection: A Deep Learning Approach.【链接】
  • Compact Convolutional Neural Network Cascade for Face Detection.【链接】
  • Face Detection with End-to-End Integration of a ConvNet and a 3D Model.【链接】
  • CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection.【链接】
  • Finding Tiny Faces.【链接】
  • Towards a Deep Learning Framework for Unconstrained Face Detection.【链接】
  • Supervised Transformer Network for Efficient Face Detection.【链接】

UnitBox

  • UnitBox: An Advanced Object Detection Network.【链接】
  • Bootstrapping Face Detection with Hard Negative Examples.【链接】
  • Grid Loss: Detecting Occluded Faces.【链接】
  • A Multi-Scale Cascade Fully Convolutional Network Face Detector.【链接】

MTCNN

  • Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks.【链接】
  • Face Detection using Deep Learning: An Improved Faster RCNN Approach.【链接】
  • Faceness-Net: Face Detection through Deep Facial Part Responses.【链接】
  • Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”.【链接】
  • End-To-End Face Detection and Recognition.【链接】
  • Face R-CNN.【链接】
  • Face Detection through Scale-Friendly Deep Convolutional Networks.【链接】
  • Scale-Aware Face Detection.【链接】
  • Multi-Branch Fully Convolutional Network for Face Detection.【链接】
  • SSH: Single Stage Headless Face Detector.【链接】
  • Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container.【链接】
  • FaceBoxes: A CPU Real-time Face Detector with High Accuracy.【链接】
  • S3FD: Single Shot Scale-invariant Face Detector.【链接】
  • Detecting Faces Using Region-based Fully Convolutional Networks.【链接】
  • AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection.【链接】

Facial Point / Landmark Detection

  • Deep Convolutional Network Cascade for Facial Point Detection.【链接】
  • Facial Landmark Detection by Deep Multi-task Learning.【链接】
  • A Recurrent Encoder-Decoder Network for Sequential Face Alignment.【链接】
  • Detecting facial landmarks in the video based on a hybrid framework.【链接】
  • Deep Constrained Local Models for Facial Landmark Detection.(https://arxiv.org/abs/1611.08657)
  • Effective face landmark localization via single deep network.【链接】
  • A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection.【链接】
  • Deep Alignment Network: A convolutional neural network for robust face alignment.【链接】
  • Joint Multi-view Face Alignment in the Wild.【链接】
  • FacePoseNet: Making a Case for Landmark-Free Face Alignment.【链接】

People Detection

  • End-to-end people detection in crowded scenes.【链接】
  • Detecting People in Artwork with CNNs.【链接】
  • Deep Multi-camera People Detection.【链接】

Person Head Detection

  • Context-aware CNNs for person head detection.【链接】

Pedestrian Detection

  • Pedestrian Detection aided by Deep Learning Semantic Tasks.【链接】
  • Deep Learning Strong Parts for Pedestrian Detection.【链接】
  • Taking a Deeper Look at Pedestrians.【链接】
  • Convolutional Channel Features.【链接】
  • Learning Complexity-Aware Cascades for Deep Pedestrian Detection.【链接】
  • Deep convolutional neural networks for pedestrian detection.【链接】
  • Scale-aware Fast R-CNN for Pedestrian Detection.【链接】
  • New algorithm improves speed and accuracy of pedestrian detection.【链接】
  • Pushing the Limits of Deep CNNs for Pedestrian Detection.【链接】
  • A Real-Time Deep Learning Pedestrian Detector for Robot Navigation.【链接】
  • A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation.【链接】
  • Is Faster R-CNN Doing Well for Pedestrian Detection?【链接】
  • Reduced Memory Region Based Deep Convolutional Neural Network Detection.【链接】
  • Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection.【链接】
  • Multispectral Deep Neural Networks for Pedestrian Detection.【链接】
  • Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters.【链接】
  • Illuminating Pedestrians via Simultaneous Detection & Segmentation.【链接】
  • Rotational Rectification Network for Robust Pedestrian Detection.【链接】
  • STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos.【链接】
  • Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy.【链接】

Vehicle Detection

  • DAVE: A Unified Framework for Fast Vehicle Detection and Annotation.【链接】
  • Evolving Boxes for fast Vehicle Detection.【链接】
  • Fine-Grained Car Detection for Visual Census Estimation.【链接】

Traffic-Sign Detection

  • Traffic-Sign Detection and Classification in the Wild.【链接】
  • Detecting Small Signs from Large Images.【链接】

Boundary / Edge / Contour Detection

  • Holistically-Nested Edge Detection.【链接】
  • Unsupervised Learning of Edges.【链接】
  • Pushing the Boundaries of Boundary Detection using Deep Learning.【链接】
  • Convolutional Oriented Boundaries.【链接】
  • Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks.【链接】
  • Richer Convolutional Features for Edge Detection.【链接】
  • Contour Detection from Deep Patch-level Boundary Prediction.【链接】
  • CASENet: Deep Category-Aware Semantic Edge Detection.【链接】

Skeleton Detection

  • Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs.【链接】
  • DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images.【链接】
  • SRN: Side-output Residual Network for Object Symmetry Detection in the Wild.【链接】

Fruit Detection

  • Deep Fruit Detection in Orchards.【链接】
  • Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards.【链接】

Part Detection

  • Objects as context for part detection.【链接】

Object Proposa

  • DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers.【链接】
  • Scale-aware Pixel-wise Object Proposal Networks.【链接】
  • Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization.【链接】
  • Learning to Segment Object Proposals via Recursive Neural Networks.【链接】
  • Learning Detection with Diverse Proposals.【链接】
  • ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond.【链接】
  • Improving Small Object Proposals for Company Logo Detection.【链接】

Localization

  • Beyond Bounding Boxes: Precise Localization of Objects in Images.【链接】
  • Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning.【链接】
  • Weakly Supervised Object Localization Using Size Estimates.【链接】
  • Active Object Localization with Deep Reinforcement Learning.【链接】
  • Localizing objects using referring expressions.【链接】
  • LocNet: Improving Localization Accuracy for Object Detection.【链接】
  • Learning Deep Features for Discriminative Localization.【链接】
  • ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization.【链接】
  • Ensemble of Part Detectors for Simultaneous Classification and Localization.【链接】
  • STNet: Selective Tuning of Convolutional Networks for Object Localization.【链接】
  • Soft Proposal Networks for Weakly Supervised Object Localization.【链接】
  • Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN.【链接】

Tutorials / Talks

  • Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection.【链接】
  • Towards Good Practices for Recognition & Detection.【链接】

Projects

  • TensorBox: a simple framework for training neural networks to detect objects in images.【链接】
  • Object detection in torch: Implementation of some object detection frameworks in torch.【链接】
  • Using DIGITS to train an Object Detection network.【链接】
  • FCN-MultiBox Detector.【链接】
  • KittiBox: A car detection model implemented in Tensorflow.【链接】
  • Deformable Convolutional Networks + MST + Soft-NMS.【链接】

Tools

  • BeaverDam: Video annotation tool for deep learning training labels.【链接】

Blogs

  • Convolutional Neural Networks for Object Detection.【链接】
  • Introducing automatic object detection to visual search (Pinterest).【链接】
  • Deep Learning for Object Detection with DIGITS.【链接】
  • Analyzing The Papers Behind Facebook’s Computer Vision Approach.【链接】
  • Easily Create High Quality Object Detectors with Deep Learning.【链接】
  • How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit.【链接】
  • Object Detection in Satellite Imagery, a Low Overhead Approach.【链接】
  • You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks.【链接】
  • Faster R-CNN Pedestrian and Car Detection.【链接】
  • Small U-Net for vehicle detection.【链接】
  • Region of interest pooling explained.【链接】
  • Supercharge your Computer Vision models with the TensorFlow Object Detection API.【链接】

(作者:朱坤)

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