Paper List

显著性检测

综述

• Salient Object Detection: A Benchmark https://arxiv.org/pdf/1501.02741

• Salient Object Detection: A Survey https://arxiv.org/pdf/1411.5878
  

  传统显著性检测

• [x] A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

• Saliency Detection: A Spectral Residual Approach
• Learning to Detect A Salient Object
• Visual Saliency Detection Based on Bayesian Model
• Saliency Detection Based on Integration of Boundary and Soft-Segmentation
• Graph-Regularized Saliency Detection With Convex-Hull-Based Center Prior
• Global Contrast based Salient Region Detection
• Bayesian Saliency via Low and Mid Level Cues
• Saliency Detection via Graph-Based Manifold Ranking
• Ranking Saliency
• Saliency Detection via Dense and Sparse Reconstruction
• Saliency Detection with Multi-Scale Superpixels

• [x] Dense and Sparse Reconstruction Error Based Saliency Descriptor

  基于深度学习的显著性检测

• [ ] Deep Visual Attention Prediction TIP2018

• Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
• Shallow and Deep Convolutional Networks for Saliency Prediction
• Saliency Detection with GAN (2017)
  • https://github.com/batsa003/salgan/ （PyTorch的版本）
• Residual Learning for Saliency Detection
• Deeply Supervised Salient Object Detection with Short Connections
• Saliency Detection with Recurrent Fully Convolutional Networks
• Learning to Detect Salient Objects with Image-level Supervision
• Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by 3D Convolutional Networks(Valse 2019)
• Deep Spectral Clustering using Dual Autoencoder Network(Valse 2019)

• [x] MEnet: A Metric Expression Network for Salient Object Segmentation(Valse 2019)

  图像分类

• [x] FishNet

• ShuffleNet（V1/V2)
• MobileNet（V1/V2)

• [ ] C3AE: Exploring the Limits of Compact Model for Age Estimation

  超分辨重建

• [ ] Meta-SR: A Magnification-Arbitrary Network for Super-Resolution

  模型压缩/加速/搜索

• [ ] A Survey of Model Compression and Acceleration for Deep Neural Networks

• CondenseNet: An Efficient DenseNet using Learned Group Convolutions

• [ ] Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search

  • https://github.com/lixincn2015/Partial-Order-Pruning

    GNN

• [x] Semi-supervised Classification with Graph Convolutional Networks

• Semantic Object Parsing with Graph LSTM

• [x] Interpretable Structure-Evolving LSTM

  分割

• [x] Semantic object parsing with graph lstm

• Interpretable structure-evolving lstm
• Large-scale point cloud semantic segmentation with superpoint graphs
• Dynamic graph cnn for learning on point clouds
• Adaptive Affinity Fields for Semantic Segmentation
• Context Encoding for Semantic Segmentation
• Dual Attention Network for Scene Segmentation
• Dilated Residual Networks
• DenseASPP for Semantic Segmentation in Street Scenes
• Understand Convolution for Semantic Segmentation
• FickleNet: Weakly and Semi-supervised Semantic Image Segmentationusing Stochastic Inference
• DUpsample: Decoders Matter for Semantic Segmentation:Data-Dependent Decoding Enables Flexible Feature Aggregation
• 3DGNN: 3D Graph Neural Networks for RGBD Semantic Segmentation
• GCN: Large Kernel Matters-Improve Semantic Segmentation by Global Convolutional Network
• DFN: Learning a Discriminative Feature Network for Semantic Segmentation
• BiSeNet: BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
• RFB: Receptive Field Block Net for Accurate and Fast Object Detection

• [ ] DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

  递归网络

• [x] Pixel Recurrent Neural Networks

• Multi-Dimensional Recurrent Neural Networks
• Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations
• Generative Image Modeling Using Spatial LSTMs

• [x] Convolutional LSTM Network: A Machine LearningApproach for Precipitation Nowcasting

  基础理论

• [ ] Foundations of Deep Learning: SGD, Overparametrization, and Generalization

  可解释人工智能

• [ ] Explanation in Human-AI Systems

  损失函数

• GIOU

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

    参考链接

• https://www.jianshu.com/p/11dbd7add4b4

• https://blog.csdn.net/touch_dream/article/details/78716507
• https://blog.csdn.net/xbcReal/article/details/53494866
• 程明明老师的建议: https://mmcheng.net/zh/paperreading/
• 目标检测论文集合: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
  • CSDN转载: https://blog.csdn.net/u013066730/article/details/82460392