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深度学习解析
- 方法论
- 论文相关
- 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
- 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
- 传统显著性检测
- 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
- Interpretable Structure-Evolving LSTM
- 递归神经网络
- 图像分割
- 视频分割
- 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
- 目标检测
- 图像恢复
- 长尾分布
- 视频行为识别
- 深度学习解析
- 指标设计
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