Busy with recent things , I'll update without day.
Notes are attached to raw PDF files from now on .
创建人 | 知乎论文阅读专栏 | 个人博客 | 其他 |
---|---|---|---|
ming71 | 论文笔记入口 | chaser | CSDN |
Update CV papers here everday .
The content includes but is not limited to Object detection , Semantic segmentation , and other papers about deep learning .
Comments are welcome , and you can e-mail me by mq_chaser@126.com .
Divided by Conference & Journal .
- M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid
- Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network
- Gradient Harmonized Single-stage Detector
- Assisted Excitation of Activations: A Learning Technique to Improve Object
- Borrow from Anywhere Pseudo Multi-modal Object Detection in Thermal Imagery
- Cascade R-CNN: Delving into High Quality Object Detection
- Feature Pyramid Networks for Object Detection
- Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
- Path Aggregation Network for Instance Segmentation
- Region Proposal by Guided Anchoring
- Scale-Transferable Object Detection
- DOTA: A Large-scale Dataset for Object Detection in Aerial Images
- R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
- Pseudo Mask Augmented Object Detection
- Single-Shot Object Detection with Enriched Semantics
- Weakly Supervised Instance Segmentation using Class Peak Response
- Learning Deep Features for Discriminative Localization
- Simple Does It: Weakly Supervised Instance and Semantic Segmentation
- Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
- Panoptic Segmentation
- Learning Instance Activation Maps for Weakly Supervised Instance Segmentation
- Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
- Single-Shot Refinement Neural Network for Object Detection
- ScratchDet : Training Single-Shot Object Detectors
- Squeeze-and-Excitation Networks
- Dual Attention Network for Scene Segmentation
- An Analysis of Scale Invariance in Object Detection
- Rotation Sensitive Regression for Oriented Scene Text Detection
- Oriented Response Networks
- DetNet: A Backbone network for Object Detection
- Receptive Field Block Net for Accurate and Fast Object Detection
- Modeling Visual Context is Key to Augmenting Object Detection Datasets
- Contextual Priming and Feedback for Faster R-CNN
- Learning to Segment via Cut-and-Paste
- Acquisition of Localization Confidence for Accurate Object Detection
- Focal Loss for Dense Object Detection
- InstaBoost: Boosting Instance Segmentation via Probability Map Guided
- Scale-Aware Trident Networks for Object Detection
- EGNet: Edge Guidance Network for Salient Object Detection
- ThunderNet: Towards Real-time Generic Object Detection
- Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
- FCOS: Fully Convolutional One-Stage Object Detection
- Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
- Single Shot Text Detector with Regional Attention
- Making Convolutional Networks Shift-Invariant Again
- How much real data do we actually need: Analyzing object detection performance using synthetic and real data (workshop)
- Why do deep convolutional networks generalize so poorly to small image transformations?
- Dataset Augmentationin In Feature Space
- ImageNet-trained CNNs are biased towards texture: increasing shape bias improves accuracy and robustness
- Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
- FSSD: Feature Fusion Single Shot Multibox Detector
- MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
- MMDetection: Open MMLab Detection Toolbox and Benchmark
- Double-Head RCNN: Rethinking Classification and Localization for Object Detection
- Learning Data Augmentation Strategies for Object Detection
- A Preliminary Study on Data Augmentation of Deep Learning for Image Classification
- Improved Regularization of Convolutional Neural Networks with Cutout
- Data Augmentation by Pairing Samples for Images Classification
- Bag of Freebies for Training Object Detection Neural Networks
- The Effectiveness of Data Augmentation in Image Classification using Deep Learning
- Natural Adversarial Examples
- Recent Advances in Deep Learning for Object Detection
- Matrix Nets: A New Deep Architecture for Object Detection
- Needles in Haystacks: On Classifying Tiny Objects in Large Images
- CBNet: A Novel Composite Backbone Network Architecture for Object Detection
- Light-Head R-CNN: In Defense of Two-Stage Object Detector
- R3Det Refined Single-Stage Detector with Feature Refinement for Rotating Object
- Beyond Skip Connections: Top-Down Modulation for Object Detection
- Deep Learning for 2D and 3D Rotatable Data An Overview of Methods
- Is Sampling Heuristics Necessary in Training Deep Object Detectors
- A Real-Time Scene Text Detector with Learned Anchor
- RAM: Residual Attention Module for Single Image Super-Resolution
- (Acess) Smart Augmentation: Learning an Optimal Data Augmentation Strategy
- (ICANN) Further advantages of data augmentation on convolutional neural networks
- (WACV) Understanding Convolution for Semantic Segmentation
- (BMVC) Enhancement of SSD by concatenating feature maps for object detection
- (Big Data) A survey on Image Data Augmentation for Deep Learning
- (DICTA) Understanding data augmentation for classification: when to warp?
- (IJCV) What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
- (ACCV) Reverse Densely Connected Feature Pyramid Network for Object Detection
- (IJAC) An Overview of Contour Detection Approaches
- (ICIP) SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes
- (Remote Sensing) Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
- (Multimedia) Arbitrary-oriented scene text detection via rotation proposals
- (NIPS) R-FCN: Object Detection via Region-based Fully Convolutional Networks
- (JMLR) Neural Architecture Search: A Survey
- (OCEANS) Ship Detection: An Improved YOLOv3 Method
- (VISIGRAPP) Learning Transformation Invariant Representations with Weak Supervision
- (ICDAR) ICDAR 2015 competition on Robust Reading
- (BMVC) Rethinking Classification and Localization for Cascade R-CNN