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传统显著性检测

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

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  • 书签
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  • 方法论
    • How to Read a Paper
    • Bag of Tricks for Image Classification
  • 论文相关
    • 论文单词
    • 微软亚洲研究院创研论坛 CVPR 2019 论文分享
    • Paper List
    • Saliency Detection
    • Something About Graph
  • 图像分类
    • Vision Transformers with Hierarchical Attention
    • ActiveMLP: An MLP-like Architecture with Active Token Mixer
    • Fast Vision Transformers with HiLo Attention
    • TRT-ViT: TensorRT-oriented Vision Transformer
    • EdgeFormer: Improving Light-weight ConvNets by Learning from Vision Transformers
    • Neighborhood Attention Transformer
    • Container: Context Aggregation Network
    • ELSA: Enhanced Local Self-Attention for Vision Transformer
    • DynaMixer: A Vision MLP Architecture with Dynamic Mixing
    • On the Integration of Self-Attention and Convolution
    • X-volution: On the Unification of Convolution and Self-attention
    • S2-MLP V1&V2 : Spatial-Shift MLP Architecture for Vision
    • CycleMLP: A MLP-like Architecture for Dense Prediction
    • RaftMLP: Do MLP-based Models Dream of Winning Over Computer Vision?
    • Hire-MLP: Vision MLP via Hierarchical Rearrangement
    • Sparse-MLP: A Fully-MLP Architecture with Conditional Computation
    • ConvMLP: Hierarchical Convolutional MLPs for Vision
    • Involution: Inverting the Inherence of Convolution for Visual Recognition
    • LambdaNetworks: Modeling long-range Interactions without Attention
    • Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
    • Attention Augmented Convolutional Networks
    • FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
  • 显著性检测
    • 传统显著性检测
      • A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
      • Learning to Detect A Salient Object
      • Ranking Saliency
      • Saliency Detection: A Spectral Residual Approach
      • Global Contrast based Salient Region Detection
      • Bayesian Saliency via Low and Mid Level Cues
      • Saliency Detection via Graph-Based Manifold Ranking
      • Visual Saliency Detection Based on Bayesian Model
      • Dense and Sparse Reconstruction Error Based Saliency Descriptor
      • Saliency Detection Based on Integration of Boundary and Soft-Segmentation
      • Saliency Detection with Multi-Scale Superpixels
      • Saliency Detection via Dense and Sparse Reconstruction
      • Graph-Regularized Saliency Detection With Convex-Hull-Based Center Prior
    • 深度显著性检测
      • 视频
        • Shifting More Attention to Video Salient Object Detection
        • Motion Guided Attention for Video Salient Object Detection
      • 图片
        • Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion
        • ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
        • Re-thinking Co-Salient Object Detection
        • CVPR2020的显著性文章一览
        • Global Context-Aware Progressive Aggregation Network for Salient Object Detection
        • F3Net: Fusion, Feedback and Focus for Salient Object Detection
        • Progressive Feature Polishing Network for Salient Object Detection
        • Towards High-Resolution Salient Object Detection
        • Selectivity or Invariance: Boundary-Aware Salient Object Detection
        • ICCV2019的显著性文章一览
        • 一些之前的论文
        • SAC-Net: Spatial Attenuation Contextfor Salient Object Detection
        • Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
        • Contour Loss: Boundary-Aware Learning for Salient Object Segmentation
        • CVPR2018的显著性文章一览
        • A Bi-directional Message Passing Model for Salient Object Detection
        • Detect Globally, Refine Locally: A Novel Approach to Saliency Detection
        • Learning to Promote Saliency Detectors
        • SE2Net: Siamese Edge-Enhancement Network for Salient Object Detection
        • Salient Object Detection: A Survey
        • CVPR2019的显著性文章一览
        • Salient Object Detection with Pyramid Attention and Salient Edges
        • Learning Uncertain Convolutional Features for Accurate Saliency Detection
        • Attentive Feedback Network for Boundary-Aware Salient Object Detection
        • MEnet: A Metric Expression Network for Salient Object Segmentation
        • Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
        • BASNet: Boundary Aware Salient Object Detection
        • Pyramid Feature Attention Network for Saliency detection
        • Learning to Detect Salient Objects with Image-level Supervision
        • Deeply Supervised Salient Object Detection with Short Connections
        • A Simple Pooling-Based Design for Real-Time Salient Object Detection
        • Saliency Detection with Recurrent Fully Convolutional Networks
        • An Iterative and Cooperative Top-down and Bottom-up Inference Network for Salient Object Detection
        • A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision
  • 图神经网络
    • Deep Learning on Graphs: A Survey
    • Graph Neural Networks: A Review of Methods and Applications
    • Learning with Local and Global Consistency
    • Ranking on Data Manifolds
    • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
    • Semi-supervised Classification with Graph Convolutional Networks
    • Semantic Object Parsing with Graph LSTM
      • 论文翻译 | Semantic Object Parsing with Graph LSTM
    • Interpretable Structure-Evolving LSTM
  • 递归神经网络
    • 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
    • Convolutional LSTM Network: A Machine LearningApproach for Precipitation Nowcasting
  • 图像分割
    • 视频分割
      • 关注Temporal Consistency的Video Model
      • ECCV 20 VOS论文一览
      • Video Object Segmentation using Space-Time Memory Networks
      • Motion-Attentive Transition for Zero-Shot Video Object Segmentation
      • See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
    • 图像分割
      • StructToken: Rethinking Semantic Segmentation with Structural Prior
      • Dense Prediction with Attentive Feature Aggregation
      • U-Net Transformer: Self and Cross Attention for Medical Image Segmentation
      • Towards Enhancing Fine-grained Details for Image Matting
      • 使用bi-directional propagation来获取全局上下文
      • Dual Super-Resolution Learning for Semantic Segmentation
      • SegFix: Model-Agnostic Boundary Refinement for Segmentation
      • Conditional Convolutions for Instance Segmentation
      • Segmenting Transparent Objects in the Wild
      • Depth与Convolution
      • Dynamic Filters for Semantic Segmentation
      • Dynamic Filter Networks
      • Dynamic Filtering with Large Sampling Field for ConvNets
      • Semi-convolutional Operators forInstance Segmentation
      • Gated Feedback Refinement Network for Dense Image Labeling
      • A Relation-Augmented Fully Convolutional Network for Semantic Segmentationin Aerial Scenes
      • Learning a Discriminative Feature Network for Semantic Segmentation
      • LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
      • Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
      • FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation
      • Superpixel Sampling Networks
      • 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
      • Decoders Matter for Semantic Segmentation:Data-Dependent Decoding Enables Flexible Feature Aggregation
      • 3D Graph Neural Networks for RGBD Semantic Segmentation
  • 生成对抗网络
    • Generative Adversarial Networks
  • 目标检测
    • 图片
      • FCOS: Fully Convolutional One-Stage Object Detection
  • 图像恢复
    • Deep Image Prior
  • 长尾分布
    • DECOUPLING REPRESENTATION AND CLASSIFIER FOR LONG-TAILED RECOGNITION
  • 视频行为识别
    • Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
  • 深度学习解析
    • NIPS 2016 | Residual Networks Behave Like Ensembles of Relatively Shallow Networks
  • 指标设计
    • Image quality assessment: from error visibility to structural similarity
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