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

• 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

• 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)

• 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

• 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

• 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)

• 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)

• 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)
• YOLACT:Real-time Instance Segmentation(YOLACT)
• Bounding Box Embedding for Single Shot Person Instance Segmentation

• 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

• 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)