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CV--PaperDaily
| 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 . Most of papers are published in recent two years
Your comments are welcome , and you can e-mail me by mq_chaser@126.com .
Paper reading divided by conference
AAAI
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid
CVPR
- 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
ECCV
- 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
ICCV
- 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
ICML
- Making Convolutional Networks Shift-Invariant Again
ICLR
- 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
ArXiv
- 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
- How much real data do we actually need: Analyzing object detection performance using synthetic and real data
- 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
Others
- (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
- (BMCV) 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