Last updated: 2019/08/30
- 2019/08/30 * -
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Feature Selective Anchor-Free Module for Single-Shot Object Detection
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Bottom-up Object Detection by Grouping Extreme and Center Points
[pytorch]
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C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
[ torch]
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MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors
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Object detection with location-aware deformable convolution and backward attention filtering
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ScratchDet: Training Single-Shot Object Detectors from Scratch
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Bounding Box Regression with Uncertainty for Accurate Object Detection
[caffe2]
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Strong-Weak Distribution Alignment for Adaptive Object Detection
[pytorch]
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NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
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Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
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Exploring the Bounds of the Utility of Context for Object Detection
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Dissimilarity Coefficient based Weakly Supervised Object Detection
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Adapting Object Detectors via Selective Cross-Domain Alignment
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Distilling Object Detectors with Fine-grained Feature Imitation
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Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations
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Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection
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Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
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Automatic adaptation of object detectors to new domains using self-training
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Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation
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Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
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Spatial-aware Graph Relation Network for Large-scale Object Detection
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Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
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[DA Faster R-CNN]Domain Adaptive Faster R-CNN for Object Detection in the Wild
[caffe]
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[SNIP]An Analysis of Scale Invariance in Object Detection – SNIP
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[Relation-Network]Relation Networks for Object Detection
[mxnet]
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[Cascade R-CNN]Cascade R-CNN: Delving into High Quality Object Detection
[caffe]
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[SIN]Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
[tensorflow]
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[RefineDet]Single-Shot Refinement Neural Network for Object Detection
[caffe]
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Finding Tiny Faces in the Wild with Generative Adversarial Network
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[MLKP]Multi-scale Location-aware Kernel Representation for Object Detection
[caffe]
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Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
[chainer]
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[Fitness NMS]Improving Object Localization with Fitness NMS and Bounded IoU Loss
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[RFBNet]Receptive Field Block Net for Accurate and Fast Object Detection
[pytorch]
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Zero-Annotation Object Detection with Web Knowledge Transfer
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[CornerNet]CornerNet: Detecting Objects as Paired Keypoints
[pytorch]
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[PFPNet]Parallel Feature Pyramid Network for Object Detection
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[Pelee]Pelee: A Real-Time Object Detection System on Mobile Devices
[caffe]
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[HKRM]Hybrid Knowledge Routed Modules for Large-scale Object Detection
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[MetaAnchor]MetaAnchor: Learning to Detect Objects with Customized Anchors
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[TDM]Beyond Skip Connections: Top-Down Modulation for Object Detection
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[YOLO v2]YOLO9000: Better, Faster, Stronger
[c]
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[RON]RON: Reverse Connection with Objectness Prior Networks for Object Detection
[caffe]
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[DeNet]DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
[theano]
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[CoupleNet]CoupleNet: Coupling Global Structure with Local Parts for Object Detection
[caffe]
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[RetinaNet]Focal Loss for Dense Object Detection
[keras]
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[Mask R-CNN]Mask R-CNN
[caffe2]
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[RSA]Recurrent Scale Approximation for Object Detection in CNN |
[caffe]
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[DSOD]DSOD: Learning Deeply Supervised Object Detectors from Scratch
[caffe]
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[SMN]Spatial Memory for Context Reasoning in Object Detection
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[Light-Head R-CNN]Light-Head R-CNN: In Defense of Two-Stage Object Detector
[tensorflow]
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[Soft-NMS]Improving Object Detection With One Line of Code
[caffe]
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[YOLO v1]You Only Look Once: Unified, Real-Time Object Detection
[c]
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[AZNet]Adaptive Object Detection Using Adjacency and Zoom Prediction
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[ION]Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
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[HyperNet]HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
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[OHEM]Training Region-based Object Detectors with Online Hard Example Mining
[caffe]
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[CRAPF]CRAFT Objects from Images
[caffe]
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[R-FCN] [NIPS' 16]R-FCN: Object Detection via Region-based Fully Convolutional Networks
[caffe]
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[PVANET]****[NIPSW' 16]PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
[caffe]
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[DeepID-Net]****[PAMI' 16]DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
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[NoC]****[TPAMI' 16]Object Detection Networks on Convolutional Feature Maps
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Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
[matlab]
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[Faster R-CNN, RPN]****[NIPS' 15]Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
[caffe]
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[MR-CNN]Object detection via a multi-region & semantic segmentation-aware CNN model
[caffe]
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[DeepBox]DeepBox: Learning Objectness with Convolutional Networks
[caffe]
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[AttentionNet]AttentionNet: Aggregating Weak Directions for Accurate Object Detection
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[Fast R-CNN]Fast R-CNN
[caffe]
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[DeepProposal]DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
[matconvnet]
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[R-CNN]Rich feature hierarchies for accurate object detection and semantic segmentation
[caffe]
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[OverFeat]****[ICLR' 14]OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
[torch]
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[MultiBox]Scalable Object Detection using Deep Neural Networks
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[SPP-Net]****[ECCV' 14]Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
[caffe]