[ICCV] InstaBoost: Boosting Instance Segmentation via Probability Map Guided
[ICCV] Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
[ICCV] Matrix Nets: A New Deep Architecture for Object Detection
[ICCV] ThunderNet: Towards Real-time Generic Object Detection
[ICCV] Towards More Robust Detection for Small, Cluttered and Rotated Objects
[ICCV] Scale-Aware Trident Networks for Object Detection
[ICCV] SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
[ICIP] SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes
[ICLR] Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
[ICLR] ImageNet-trained CNNs are biased towards texture: increasing shape bias improves accuracy and robustness
[ICLR] Why do deep convolutional networks generalize so poorly to small image transformations?
[ICML] How much real data do we actually need: Analyzing object detection performance
using synthetic and real data
[ICML] Making Convolutional Networks Shift-Invariant Again
[ICTAI] Twin Feature Pyramid Networks for Object Detection
[IEEE Access] A Real-Time Scene Text Detector with Learned Anchor
[IEEE Trans Geosci Remote Sens] CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
[IJCAI] Omnidirectional Scene Text Detection with Sequential-free Box Discretization
[J. Big Data] A survey on Image Data Augmentation for Deep Learning
[NeurIPS] Cascade RPN Delving into High-Quality Region Proposal Network with Adaptive Convolution
[NeurIPS] FreeAnchor Learning to Match Anchors for Visual Object Detection
A Preliminary Study on Data Augmentation of Deep Learning for Image Classification
Bag of Freebies for Training Object Detection Neural Networks
Consistent Optimization for Single-Shot Object Detection
Deep Learning for 2D and 3D Rotatable Data An Overview of Methods
Double-Head RCNN: Rethinking Classification and Localization for Object Detection
IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection
IoU-uniform R-CNN: Breaking Through the Limitations of RPN
Is Sampling Heuristics Necessary in Training Deep Object Detectors
Learning Data Augmentation Strategies for Object Detection
Learning from Noisy Anchors for One-stage Object Detection
Light-Head R-CNN: In Defense of Two-Stage Object Detector
MMDetection: Open MMLab Detection Toolbox and Benchmark
Multi-Scale Attention Network for Crowd Counting
Natural Adversarial Examples
Needles in Haystacks: On Classifying Tiny Objects in Large Images
Revisiting Feature Alignment for One-stage Object Detection
Ship Detection: An Improved YOLOv3 Method
2018
[ACCV] Reverse Densely Connected Feature Pyramid Network for Object Detection
[BMVC] Enhancement of SSD by concatenating feature maps for object detection
[CVPR] An Analysis of Scale Invariance in Object Detection
[CVPR] Cascade R-CNN: Delving into High Quality Object Detection
[CVPR] DOTA: A Large-scale Dataset for Object Detection in Aerial Images
[CVPR] Path Aggregation Network for Instance Segmentation
[CVPR] Pseudo Mask Augmented Object Detection
[CVPR] Rotation Sensitive Regression for Oriented Scene Text Detection
[CVPR] Scale-Transferable Object Detection
[CVPR] Single-Shot Object Detection with Enriched Semantics
[CVPR] Single-Shot Refinement Neural Network for Object Detection
[CVPR] Squeeze-and-Excitation Networks
[CVPR] Weakly Supervised Instance Segmentation using Class Peak Response
[ECCV] Acquisition of Localization Confidence for Accurate Object Detection
[ECCV] Deep Feature Pyramid Reconfiguration for Object Detection
[ECCV] DetNet: A Backbone network for Object Detection
[ECCV] Learning to Segment via Cut-and-Paste
[ECCV] Modeling Visual Context is Key to Augmenting Object Detection Datasets
[ECCV] Receptive Field Block Net for Accurate and Fast Object Detection
[ICLR] Multi-Scale Dense Convolutional Networks for Efficient Prediction
[ICANN] Further advantages of data augmentation on convolutional neural networks
[ISBI] A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
[TIP] TextBoxes++: A single-shot oriented scene text detector
[TMM] Arbitrary-oriented scene text detection via rotation proposals
[IJAC] An Overview of Contour Detection Approaches
[IJCV] What Makes Good Synthetic Training Data for Learning Disparity and Optical
Flow Estimation?
[J Mach Learn Res] Neural Architecture Search: A Survey
[Remote Sens.] Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
[VISIGRAPP] Learning Transformation Invariant Representations with Weak Supervision
[WACV] Understanding Convolution for Semantic Segmentation
Data Augmentation by Pairing Samples for Images Classification
MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
RAM: Residual Attention Module for Single Image Super-Resolution
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
2017
[AAAI] Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network
[CVPR] Feature Pyramid Networks for Object Detection
[CVPR] Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
[CVPR] Oriented Response Networks
[CVPR] Simple Does It: Weakly Supervised Instance and Semantic Segmentation
[ICCV] Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
[ICCV] Focal Loss for Dense Object Detection
[ICCV] Grad-CAM Visual Explanations From Deep Networks via Gradient-Based Localization
[ICCV] Single shot scale-invariant face detector
[ICCV] Single Shot Text Detector with Regional Attention
[ICIP] Rotated region based CNN for ship detection
[ICLR] Dataset Augmentationin In Feature Space
[ICPRAM] A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines
[IEEE Acess] Smart Augmentation: Learning an Optimal Data Augmentation Strategy
FSSD: Feature Fusion Single Shot Multibox Detector
Improved Regularization of Convolutional Neural Networks with Cutout
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Tversky loss function for image segmentation using 3D fully convolutional deep networks
2016
[CVPR] Learning Deep Features for Discriminative Localization
[DICTA] Understanding data augmentation for classification: when to warp?
[ECCV] Contextual Priming and Feedback for Faster R-CNN
[NIPS] R-FCN: Object Detection via Region-based Fully Convolutional Networks
[GRSL] Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds
Beyond Skip Connections: Top-Down Modulation for Object Detection
2015
[ICDAR] ICDAR 2015 competition on Robust Reading
2014
[CVPR] Scalable Object Detection Using Deep Neural Networks
2012
[PAMI] Measuring the Objectness of Image Windows
2009
[ICML] Curriculum learning
2000
[IJCV] The earth mover's distance as a metric for image retrieval