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sod-cnns-based-read-list's Introduction

SOD CNNs-based Read List

Sort out some saliency methods (2D RGB, 2D RGB-D, 360 RGB, Video SOD) and summarize (Code and Paper).

Keywords

obd. : Object Detection sod. : SOD seg. : segmentation depe. : Depth Estimation rgbd. : RGB-D 360. : 360° image suv. : survey com. : compression eval. : evaluate metrics

Papers

2015

NO. Keyword Title Paper Code
01 suv. What is a Salient Object? A Dataset and a Baseline Model for Salient Object Detection IEEE -
02 suv. Salient Object Detection: A Benchmark IEEE C++ & Matlab

2017

NO. Keywords Title Paper Code
01 obd. Feature Pyramid Networks for Object Detection CVPR -
02 seg. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs IEEE Tensorflow
03 - Look around you: Saliency maps for omnidirectional images in VR applications IEEE -
04 eval. Structure-measure: A New Way to Evaluate Foreground Maps IEEE MatLab / Python

2018

NO. Keywords Title Paper Code
01 obd. 360. Object Detection in Equirectangular Panorama CVPR -
02 360. Saliency Detection in 360° Videos ECCV PyTorch
03 eval. Enhanced-alignment Measure for Binary Foreground Map Evaluation IJCAI MatLab

2019

NO. Keywords Title Paper Code
01 sod. A Simple Pooling-Based Design for Real-Time Salient Object Detection CVPR PyTorch
02 sod. Cascaded Partial Decoder for Fast and Accurate Salient Object Detection CVPR PyTorch
03 360. HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation CVPR PyTorch

2020

NO. Keywords Title Paper Code
01 depe. 360. BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion CVPR PyTorch
02 360. SalBiNet360: Saliency Prediction on 360° Images with Local-Global Bifurcated Deep Network IEEE VR -
03 sod. 360. Distortion-Adaptive Salient Object Detection in 360∘ Omnidirectional Images IEEE Caffe
04 sod. 360. Stage-wise Salient Object Detection in 360° Omnidirectional Image via Object-level Semantical Saliency Ranking IEEE PyTorch
05 sod. 360. FANet: Features Adaptation Network for 360° Omnidirectional Salient Object Detection IEEE PyTorch

2021

NO. Keywords Title Paper Code
01 rgbd. sod. Calibrated RGB-D Salient Object Detection CVPR PyTorch
02 rgbd. Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion CVPR -
03 rgbd. Uncertainty Inspired RGB-D Saliency Detection CVPR PyTorch
04 depe. 360. HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features CVPR PyTorch
05 depe. 360. UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation CVPR PyTorch
06 seg. Fully Convolutional Networks for Panoptic Segmentation CVPR Detectron2
07 360. 一种立体全景图像显著性检测模型 激光与光电子学进展 -
08 com. 360. OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression arxiv -

Datasets

  • 360°
    • Fixation Prediction
    • Salient Object Detection
      • [360-SOD] : indoor & outdoor, 400 training images and 100 test images. (Details)
      • [F-360iSOD] : The F-360iSOD is a small-scale 360◦ dataset with totally 107 panoramic images collected from Stanford360 [1] and Salient!360 [2] which contain 85 and 22 equirectangular images, respectively. (Details)
      • [360-SSOD] : construct a novel dataset containing 1,105 360° omnidirectional images with pixel-wise saliency annotations; to thebest of our knowledge, this is the largest dataset for the 360° SOD problem.(Details)
      • [ASOD60K] : To facilitate the study of panoramic video salient object detection (PV-SOD), collect ASOD60K, the first large-scale PV-SOD dataset providing professional annotations, which consists of 62,455 high-resolution (4K) video frames from 67 carefully selected 360° panoramic video sequences. 10,465 key frames are annotated with rich labels, namely, super-class, sub-class, attributes, HM data, eye fifixations, bounding boxes, object masks, and instance masks. (Details)
    • (pending)
  • 2D（pending）
    • [MSRA10K] : formally named as THUS10000; 195MB: images + binary masks. Pixel accurate salient object labeling for 10000 images from MSRA dataset.
    • [THUR15K] : 15000 images.
    • [ECSSD] : 1000 images.
    • [ Judd ] : 900 images.
    • [SED1/2] : 200个灰度图像以及真实标注分割.
    • [DUT-OMRON] : 5168 images.
    • [DUTS] : 10,553 training images and 5,019 test images.
    • [HKU-IS] : 4447个具有显着对象的像素注释的图像.
    • [PASCAL-S] : 850 images.

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