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Image segmentation-Object detection-Light-weight network

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semantic segmentation/object detection/light-weight network/instance segmentation

Deep-base-network

  • ImageNet Classification with Deep Convolutional Neural Networks(AlexNet)
  • Very Deep Convolutional Networks For Large-Scale Image Recognition(VGG)
  • Network In Network(NIN)
  • Going Deeper with Convolutions(GoogleNet)
  • Deep Residual Learning for Image Recognition(ResNet)
  • Densely Connected Convolutional Networks(DenseNet)
  • Squeeze-and-Excitation Networks(SENet)
  • Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks(GENet)
  • Non-local Neural Networks
  • Convolutional Neural Networks with layer reuse(LruNet)
  • GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond(GCNet)
  • Rethinking ImageNet Pre-training
  • Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation

light-weight network

  • SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size(SqueezeNet)
  • Mobilenets: Efficient convolutional neural networks for mobile vision applications(Mobilenet V1)
  • ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices(ShuffleNet V1)
  • Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation(Mobilenet V2)
  • SqueezeNext: Hardware-Aware Neural Network Design(SqueezeNext)
  • CondenseNet: An Efficient DenseNet using Learned Group Convolutions(CondenseNet)
  • Pelee: A Real-Time Object Detection System on Mobile Devices(PeleeNet)
  • ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design(ShuffleNet V2)
  • ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation(ESPNet)
  • ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions(ChannelNets)
  • ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network(ESPNetV2)
  • Interleaved Group Convolutions for Deep Neural Networks(IGCV1)
  • IGCV2: Interleaved Structured Sparse Convolutional Neural Networks(IGCV2)
  • IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks(IGCV3)
  • MnasNet: Platform-Aware Neural Architecture Search for Mobile(MnasNet)
  • FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search(FBNet)
  • EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks(EfficientNet)
  • DiCENet: Dimension-wise Convolutions for Efficient Networks(DiCENet)
  • Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition
  • An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

semantic segmentation

  • Fully Convolutional Networks for Semantic Segmentation(FCN)
  • SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation(SegNet)
  • U-Net: Convolutional Networks for Biomedical Image Segmentation(UNet)
  • Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs(Deeplab v1)
  • DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution,and Fully Connected CRFs(Deeplab v2)
  • Understanding Convolution for Semantic Segmentation(DUC)
  • Pyramid Scene Parsing Network(PSPNet)
  • Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network(GCN)
  • Rethinking Atrous Convolution for Semantic Image Segmentation(Deeplab v3)
  • DenseASPP for Semantic Segmentation in Street Scenes(DenseASPP
  • Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(Deeplab v3plus
  • Context Encoding for Semantic Segmentation(EncNet)
  • Learning a Discriminative Feature Network for Semantic Segmentation(DFN)
  • Smoothed Dilated Convolutions for Improved Dense Prediction(SDC)
  • Pyramid Attention Network for Semantic Segmentation(PAN)
  • Exploring Context with Deep Structured models for Semantic Segmentation(FeatMap-Net)
  • ExFuse: Enhancing Feature Fusion for Semantic Segmentation(ExFuse)
  • Dilated Residual Networks(DRN)
  • Dual Attention Network for Scene Segmentation(DANet)
  • OCNet:Object Context Network for Scene Parsing(OCNet)
  • RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation(RefineNet)
  • Dense Relation Network: Learning Consistent And Context-Aware Prepresentation For Semantic Image Segmentation(DRN)
  • CCNet: Criss-Cross Attention for Semantic Segmentation(CCNet)
  • Unified Perceptual Parsing for Scene Understanding(UPerNet)
  • Tree-structured Kronecker Convolutional Networks for Semantic Segmentation(TKNet)
  • NeuroIoU: Learning a Surrogate Loss for Semantic Segmentation(NeuroIoU)
  • Decoders Matter for Semantic Segmentation:Data-Dependent Decoding Enables Flexible Feature Aggregation
  • GFF: Gated Fully Fusion for Semantic Segmentation(GFF
  • Learning Fully Dense Neural Networks for Image Semantic Segmentation(FDNet
  • ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation(ZigZagNet)
  • Adaptive Pyramid Context Network for Semantic Segmentation(APCNet)
  • Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
  • ACFNet: Attentional Class Feature Network for Semantic Segmentation(ACFNet)
  • Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images
  • Dual Graph Convolutional Network for Semantic Segmentation
  • Global Aggregation then Local Distribution in Fully Convolutional Networks
  • Dynamic Multi-scale Filters for Semantic Segmentation
  • Unifying Training and Inference for Panoptic Segmentation
  • Semantic Flow for Fast and Accurate Scene Parsing
  • AlignSeg: Feature-Aligned Segmentation Networks
  • Cars Can’t Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks
  • Context Prior for Scene Segmentation
  • Learning Statistical Texture for Semantic Segmentation

fast/real-time segmentation

  • ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation(ENet)
  • ICNet for Real-Time Semantic Segmentation(ICNet)
  • BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation(BiSeNet)
  • LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation(LinkNet)
  • Rtseg: Real-Time Semantic Segmentation Comparative Study
  • Shuffleseg: Real-Time Semantic Segmentation Network(Shuffleseg)
  • ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation(ESPNet)
  • Light-Weight RefineNet for Real-Time Semantic Segmentation(Light-Weight RefineNet)
  • LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation(LinkNet)
  • D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction(D-LinkNet)
  • CGNet: A Light-weight Context Guided Network for Semantic Segmentation(CGNet)
  • Efficient ConvNet for Real-time Semantic Segmentation
  • A Comparative Study of Real-time Semantic Segmentation for Autonomous Driving
  • ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time(ContextNet)
  • ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network(ESPNetV2)
  • ShelfNet for Real-time semantic segmentation(ShelfNet)
  • ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation(ERFNet)
  • Concentrated-Comprehensive Convolutions for lightweight semantic segmentation(CCCNet)
  • DSNet for Real-Time Driving Scene Semantic Segmentation(DSNet)
  • Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation(EDANet)
  • Fast-SCNN: Fast Semantic Segmentation Network(Fast-SCNN)
  • Guided Upsampling Network for Real-Time Semantic Segmentation(GUN)
  • In Defense of Pre-trained ImageNet Architecturesfor Real-time Semantic Segmentation of Road-driving Images(SwiftNetRN)
  • Residual Pyramid Learning for Single-Shot Semantic Segmentation(RPNet)
  • DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation(DFANet)
  • DSNet: An Efficient CNN for Road Scene Segmentation(DSNet)
  • Spatial Sampling Network for Fast Scene Understanding
  • RGPNET: A REAL-TIME GENERAL PURPOSE SEMANTIC SEGMENTATION
  • LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
  • FASTERSEG: SEARCHING FOR FASTER REAL-TIME SEMANTIC SEGMENTATION
  • Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
  • Customizable Architecture Search for Semantic Segmentation
  • Semantic Flow for Fast and Accurate Scene Parsing
  • BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
  • ASNet: Aggregated Scale Transformations for Real-Time Semantic Segmentation

Deep object detection

  • Rich feature hierarchies for accurate object detection and semantic segmentation(R-CNN)
  • SSD: Single Shot MultiBox Detector(SSD)
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks(Faster R-CNN)
  • Feature Pyramid Networks for Object Detection(FPN)
  • Is Faster R-CNN Doing Well for Pedestrian Detection?(RPN_BF)
  • Training Region-based Object Detectors with Online Hard Example Mining(OHEM)
  • Receptive Field Block Net for Accurate and Fast Object Detection(RFBNet)
  • Focal Loss for Dense Object Detection(RetinaNet)
  • Single-Shot Refinement Neural Network for Object Detection(RefinDet)
  • PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection(PVANET)
  • Multi-label learning of part detectors for heavily occluded pedestrian detection(JL-TopS)
  • Graininess-aware Deep Feature Learning for Pedestrian Detection(GDFL)
  • M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network(M2Det)
  • CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving(CFENet)
  • ScratchDet: Training Single-Shot Object Detectors from Scratch(ScratchDet)
  • Pooling Pyramid Network for Object Detection(PPN
  • ThunderNet: Towards Real-time Generic Object Detection(ThunderNet)
  • Light-Weight RetinaNet for Object Detection
  • CornerNet: Detecting Objects as Paired Keypoints(CornerNet)
  • Bottom-up Object Detection by Grouping Extreme and Center Points(ExtremeNet)
  • RepPoints: Point Set Representation for Object Detection(RepPoints)
  • FCOS: Fully Convolutional One-Stage Object Detection(FCOS)
  • Mask-Guided Attention Network for Occluded Pedestrian Detection
  • Learning Rich Features at High-Speed for Single-Shot Object Detection.
  • Dynamic Anchor Feature Selection for Single-Shot Object Detection.
  • Contextual Attention for Hand Detection in the Wild
  • Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
  • Multiple Anchor Learning for Visual Object Detection
  • NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection
  • Is Sampling Heuristics Necessary in Training Deep Object Detectors?
  • Rethinking Classification and Localization for Object Detection
  • Multiple Anchor Learning for Visual Object Detection
  • Learning from Noisy Anchors for One-stage Object Detection
  • Learning a Unified Sample Weighting Network for Object Detection∗
  • D2Det: Towards High Quality Object Detection and Instance Segmentation
  • AugFPN: Improving Multi-scale Feature Learning for Object Detection
  • Scale-Equalizing Pyramid Convolution for Object Detection

Face Detection

  • S3FD: Single Shot Scale-invariant Face Detector(SFD)
  • FaceBoxes: A CPU Real-time Face Detector with High Accuracy(FaceBoxes)
  • Detecting Face with Densely Connected Face Proposal Network(DCFPN)
  • SSH: Single Stage Headless Face Detector(SSH)
  • DSFD: Dual Shot Face Detector(DSFD)
  • Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks(MTCNN)
  • PyramidBox: A Context-assisted Single Shot Face Detector(PyramidBox)
  • SRN:Selective Refinement Network for High Performance Face Detection(SRN)
  • Single Shot Attention-Based Face Detector(AFN)
  • Improved Selective Refinement Network for Face Detection(ISRN)
  • PyramidBox++: High Performance Detector for Finding Tiny Face(PyramidBox++)
  • RetinaFace: Single-stage Dense Face Localisation in the Wild(RetinaFace)

Instance segmentation

  • Fully Convolutional Instance-aware Semantic Segmentation(FCIS)
  • Instance-aware Semantic Segmentation via Multi-task Network Cascades(MNC)
  • Mask R-CNN
  • Mask Scoring R-CNN
  • Path Aggregation Network for Instance Segmentation(PANet)
  • RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free(RetinaMask)
  • YOLACT Real-time Instance Segmentation(YOLACT)
  • Parsing R-CNN for Instance-Level Human Analysis(Parsing R-CNN)
  • BlitzNet: A Real-Time Deep Network for Scene Understanding(BlitzNet)
  • Hybrid Task Cascade for Instance Segmentation(HTC)
  • Triply Supervised Decoder Networks for Joint Detection and Segmentation(TripleNet)
  • ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation(ZigZagNet)
  • Bounding Box Embedding for Single Shot Person Instance Segmentation
  • Shape-aware Feature Extraction for Instance Segmentation
  • Real-Time Panoptic Segmentation from Dense Detections
  • EmbedMask: Embedding Coupling for One-stage Instance Segmentation
  • PolyTransform: Deep Polygon Transformer for Instance Segmentation
  • SOLO: Segmenting Objects by Locations
  • RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
  • SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
  • YOLACT++:Better Real-time Instance Segmentation
  • SAIS: Single-stage Anchor-free Instance Segmentation
  • PolarMask: Single Shot Instance Segmentation with Polar Representation
  • BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation

Mutil-task learning

  • End-to-End Multi-Task Learning with Attention.
  • Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics.
  • BlitzNet: A Real-Time Deep Network for Scene Understanding.
  • Triply Supervised Decoder Networks for Joint Detection and Segmentation
  • Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving.
  • Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation.
  • GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Network
  • MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
  • MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
  • Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
  • MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
  • AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery

non-deep object detection

  • Robust Real-Time Face Detection(Haar+Adaboost)
  • Integral Channel Features(ICF)
  • The Fastest Pedestrian Detector in the West(FPDW)
  • Fast Feature Pyramids for Object Detection(ACF)
  • Local Decorrelation for Improved Pedestrian Detection(LDCF)
  • Convolutional Channel Features(CCF)
  • Informed Haar-like Features Improve Pedestrian Detection(InformedHaar)
  • Fast Pedestrian Detection for Mobile Devices(FastCF)
  • Pedestrian detection at 100 Frames Per Second(VeryFast)
  • To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection(ACF+/LDCF+)
  • Filtered channel features for pedestrian detection(Checkerboard)
  • Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry(NNNF)
  • Aggregate Channel Features for Multi-view Face Detection(ACFFace)
  • Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning(SpatialPooling+)
  • BAdaCost: Multi-class Boosting with Costs(BAdaCost)
  • Exploring Prior Knowledge for Pedestrian Detection(SCCPriors)
  • A Fast, Modular Scene Understanding System using Context-Aware Object Detection(SC-ACF
  • Ten Years of Pedestrian Detection,What Have We Learned?(Katamari)
  • How Far are We from Solving Pedestrian Detection?
  • What Can Help Pedestrian Detection?
  • Taking a Deeper Look at Pedestrians
  • Semantic Channels for Fast Pedestrian Detection(MRFC+Semantic)
  • Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features
  • Learning Multilayer Channel Features for Pedestrian Detection
  • Fast and Robust Object Detection Using Visual Subcategories
  • Learning to Detect Vehicles by Clustering Appearance Patterns(Subcat)
  • Looking at Pedestrians at Different Scales: A Multiresolution Approach and Evaluations(MR-ACF)
  • Multiresolution models for object detection
  • Face Detection without Bells and Whistles
  • Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework
  • An Exploration of Why and When Pedestrian Detection Fails
  • Discriminative Sub-categorization

Image Stitching

  • Automatic Panoramic Image Stitching Using Invariant Features(IJCV2007)
  • As-Projective-As-Possible Image Stitching with Moving DLT(APAP)
  • Shape-Preserving Half-Projective Warps for Image Stitching(SPHP)
  • Adaptive As-Natural-As-Possible Image Stitching(AANAP)
  • MAGSAC: marginalizing sample consensus
  • MAGSAC++, a fast, reliable and accurate robust estimator
  • An Evaluation of Feature Matchers for Fundamental Matrix Estimation
  • GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature correspondence
  • Vanishing Point Guided Natural Image Stitching
  • Warping Residual Based Image Stitching for Large Parallax

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