Object Detection.md

Object Detection

arXiv of Object Detection

• Learning Data Augmentation Strategies for Object Detection

• EfficientDet: Scalable and Efficient Object Detection

• Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos

• Consistent Scale Normalization for Object Recognition

• R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

2020 ICLR of Object Detection

• Computation Reallocation for Object Detection

2020 AAAI of Object Detection

• Spiking-YOLO: Spiking Neural Network for Real-time Object Detection

• Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction

• Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

2019 NIPS of Object Detection

• One-Shot Object Detection with Co-Attention and Co-Excitation

• Memory-oriented Decoder for Light Field Salient Object Detection

• FreeAnchor: Learning to Match Anchors for Visual Object Detection

• Consistency-based Semi-supervised Learning for Object detection

• DetNAS: Backbone Search for Object Detection [pytorch]

• Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution [pytorch]

• FreeAnchor: Learning to Match Anchors for Visual Object Detection [pytorch]

• Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection

2019 ICCV of Object Detection

• ThunderNet: Towards Real-time Generic Object Detection

• Multi-adversarial Faster-RCNN for Unrestricted Object Detection

• FCOS: Fully Convolutional One-Stage Object Detection [pytorch]

• Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection

• Towards Adversarially Robust Object Detection

• Few-shot Object Detection via Feature Reweighting

• Meta-Learning to Detect Rare Objects

• C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection

• Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes

• Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection

• Weakly Supervised Object Detection With Segmentation Collaboration

• Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection

• POD: Practical Object Detection With Scale-Sensitive Network

• Enriched Feature Guided Refinement Network for Object Detection

• NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

• Sequence Level Semantics Aggregation for Video Object Detection

• Optimizing the F-Measure for Threshold-Free Salient Object Detection

• EGNet: Edge Guidance Network for Salient Object Detection

• Objects365: A Large-Scale, High-Quality Dataset for Object Detection

• Few-Shot Object Detection via Feature Reweighting

• Towards Precise End-to-End Weakly Supervised Object Detection Network

• WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection

• Learning to Rank Proposals for Object Detection

• Semi-Supervised Video Salient Object Detection Using Pseudo-Labels

• Motion Guided Attention for Video Salient Object Detection

• Stacked Cross Refinement Network for Edge-Aware Salient Object Detectioncode

• Learning Lightweight Lane Detection CNNs by Self Attention Distillation [tensorflow]

• SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects [pytorch]

• Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection [pytorch]

• Clustered Object detection in aerial images

• Relation Distillation Networks for Video Object Detection

• Scaling Object Detection by Transferring Classification Weights [pytorch]

• GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition

• Towards Unconstrained End-to-End Text Spotting

• RepPoints: Point Set Representation for Object Detection [pytorch]

• Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving[C]

• Dynamic Anchor Feature Selection for Single-Shot Object Detection

• CenterNet: Keypoint Triplets for Object Detection [pytorch]

• Towards High-Resolution Salient Object Detection

• Fast Object Detection in Compressed Video

• Relation Distillation Networks for Video Object Detection

• Object Guided External Memory Network for Video Object Detection

• Multi-Adversarial Faster-RCNN for Unrestricted Object Detection

• Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification

• Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection

• Transductive Learning for Zero-Shot Object Detection

• Generative Modeling for Small-Data Object Detection

• Scale-Aware Trident Networks for Object Detection

• Towards Interpretable Object Detection by Unfolding Latent Structures

• Minimum Delay Object Detection From Video

• Progressive Sparse Local Attention for Video Object Detection

• Selectivity or Invariance: Boundary-Aware Salient Object Detection

• Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection

• Learning Rich Features at High-Speed for Single-Shot Object Detection

• mploying Deep Part-Object Relationships for Salient Object Detection

• Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

• A Delay Metric for Video Object Detection: What Average Precision Fails to Tell

• A Robust Learning Approach to Domain Adaptive Object Detection

• Multi-adversarial Faster-RCNN for Unrestricted Object Detection

• Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection(BMVC 19)

• Soft Sampling for Robust Object Detection(BMVC 19)

2019 CVPR of Object Detection

• Feature Selective Anchor-Free Module for Single-Shot Object Detection

• Bottom-up Object Detection by Grouping Extreme and Center Points [pytorch]

• C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection [ torch]

• MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors

• You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection

• Object detection with location-aware deformable convolution and backward attention filtering

• ScratchDet: Training Single-Shot Object Detectors from Scratch

• Bounding Box Regression with Uncertainty for Accurate Object Detection [caffe2]

• Activity Driven Weakly Supervised Object Detection

• Towards Accurate One-Stage Object Detection with AP-Loss

• Strong-Weak Distribution Alignment for Adaptive Object Detection [pytorch]

• NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

• Adaptive NMS: Refining Pedestrian Detection in a Crowd

• Point in, Box out: Beyond Counting Persons in Crowds

• Locating Objects Without Bounding Boxes

• Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects

• Towards Universal Object Detection by Domain Attention

• Exploring the Bounds of the Utility of Context for Object Detection

• What Object Should I Use? Task Driven Object Detection

• Dissimilarity Coefficient based Weakly Supervised Object Detection

• Adapting Object Detectors via Selective Cross-Domain Alignment

• Fully Quantized Network for Object Detection

• Distilling Object Detectors with Fine-grained Feature Imitation

• Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations

• Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection

• Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

• Automatic adaptation of object detectors to new domains using self-training

• Libra R-CNN: Balanced Learning for Object Detection

• Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation

• Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors

• Spatial-aware Graph Relation Network for Large-scale Object Detection

• Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection

2019 ICLR of Object Detection

• CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild

• Feature Intertwiner for Object Detection

2019 AAAI of Object Detection

• M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network [pytorch]

• Object Detection based on Region Decomposition and Assembly

2018 CVPR of Object Detection

• [DA Faster R-CNN]Domain Adaptive Faster R-CNN for Object Detection in the Wild [caffe]

• [SNIP]An Analysis of Scale Invariance in Object Detection – SNIP

• [Relation-Network]Relation Networks for Object Detection [mxnet]

• [Cascade R-CNN]Cascade R-CNN: Delving into High Quality Object Detection [caffe]

• [SIN]Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships [tensorflow]

• [STDN]Scale-Transferrable Object Detection

• [RefineDet]Single-Shot Refinement Neural Network for Object Detection [caffe]

• [MegDet]MegDet: A Large Mini-Batch Object Detector

• Finding Tiny Faces in the Wild with Generative Adversarial Network

• [MLKP]Multi-scale Location-aware Kernel Representation for Object Detection [caffe]

• Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [chainer]

• [Fitness NMS]Improving Object Localization with Fitness NMS and Bounded IoU Loss

2018 ECCV of Object Detection

• [RFBNet]Receptive Field Block Net for Accurate and Fast Object Detection [pytorch]

• Zero-Annotation Object Detection with Web Knowledge Transfer

• [CornerNet]CornerNet: Detecting Objects as Paired Keypoints [pytorch]

• [PFPNet]Parallel Feature Pyramid Network for Object Detection

2018 NIPS of Object Detection

• [Pelee]Pelee: A Real-Time Object Detection System on Mobile Devices [caffe]

• [HKRM]Hybrid Knowledge Routed Modules for Large-scale Object Detection

• [MetaAnchor]MetaAnchor: Learning to Detect Objects with Customized Anchors

• [SNIPER]SNIPER: Efficient Multi-Scale Training

2017 CVPR of Object Detection

• [DSSD]DSSD : Deconvolutional Single Shot Detector [caffe]

• [TDM]Beyond Skip Connections: Top-Down Modulation for Object Detection

• [FPN]Feature Pyramid Networks for Object Detection

• [YOLO v2]YOLO9000: Better, Faster, Stronger [c]

• [RON]RON: Reverse Connection with Objectness Prior Networks for Object Detection [caffe]

2017 ICCV of Object Detection

• [DeNet]DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling [theano]

• [CoupleNet]CoupleNet: Coupling Global Structure with Local Parts for Object Detection [caffe]

• [RetinaNet]Focal Loss for Dense Object Detection [keras]

• [Mask R-CNN]Mask R-CNN [caffe2]

• [RSA]Recurrent Scale Approximation for Object Detection in CNN | [caffe]

• [DCN]Deformable Convolutional Networks [mxnet]

• [DSOD]DSOD: Learning Deeply Supervised Object Detectors from Scratch [caffe]

• [SMN]Spatial Memory for Context Reasoning in Object Detection

• [Light-Head R-CNN]Light-Head R-CNN: In Defense of Two-Stage Object Detector [tensorflow]

• [Soft-NMS]Improving Object Detection With One Line of Code [caffe]

2016 CVPR of Object Detection

• [YOLO v1]You Only Look Once: Unified, Real-Time Object Detection [c]

• [G-CNN]G-CNN: an Iterative Grid Based Object Detector

• [AZNet]Adaptive Object Detection Using Adjacency and Zoom Prediction

• [ION]Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

• [HyperNet]HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

• [OHEM]Training Region-based Object Detectors with Online Hard Example Mining [caffe]

• [CRAPF]CRAFT Objects from Images [caffe]

2016 ECCV of Object Detection

• [SSD]SSD: Single Shot MultiBox Detector [caffe]

• [GBDNet]Crafting GBD-Net for Object Detection [caffe]

• [CPF]Contextual Priming and Feedback for Faster R-CNN

• [MS-CNN]A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection [caffe]

2016 NIP and PAMI of Object Detection

• [R-FCN] [NIPS' 16]R-FCN: Object Detection via Region-based Fully Convolutional Networks [caffe]

• [PVANET] [NIPSW' 16]PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection [caffe]

• [DeepID-Net] [PAMI' 16]DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

• [NoC] [TPAMI' 16]Object Detection Networks on Convolutional Feature Maps

2015 CVPR and NIPS of Object Detection

• Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction [matlab]

• [Faster R-CNN, RPN] [NIPS' 15]Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [caffe]

2015 ICCV of Object Detection

• [MR-CNN]Object detection via a multi-region & semantic segmentation-aware CNN model [caffe]

• [DeepBox]DeepBox: Learning Objectness with Convolutional Networks [caffe]

• [AttentionNet]AttentionNet: Aggregating Weak Directions for Accurate Object Detection

• [Fast R-CNN]Fast R-CNN [caffe]

• [DeepProposal]DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers [matconvnet]

2014 CVPR & ICLR & ECCV of Object Detection

• [R-CNN]Rich feature hierarchies for accurate object detection and semantic segmentation [caffe]

• [OverFeat][ICLR' 14]OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks [torch]

• [MultiBox]Scalable Object Detection using Deep Neural Networks

• [SPP-Net][ECCV' 14]Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition [caffe]