代码| https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency [10].RGB-D Salient Object Detection with Cross-Modality Modulation and Selection 作者| Chongyi Li, Runmin Cong, Yongri Piao, Qianqian Xu, Chen Change Loy 单位| 南洋理工大学;北京交通大学;大连理工大学;中科院 论文| https://arxiv....
a set of upsampling operations (H) is adopted in order toenlarge all salient object estimations and salient edge informationfrom all preceding layers with current feature resolutions(密集连接前调整分辨率). We then update the saliency representation Y through Eq.7. Next,the edge detection module F ...
Salient object detection: A discriminative regional feature integration approach. In CVPR, 2013. 6, 7 [15] J. Kim, D. Han, Y.-W. Tai, and J. Kim. Salient region detection via high-dimensional color transform. In CVPR, 2014. 2, 6 [16] K. Koehler, F. Guo, S. Zhang, and M. P...
比如鸟都有尖尖的嘴；salient object detection中显著物体类别中可能包含鸟、猫、火车、瓶子等不同的物体...
Co-Salient目标检测英文全称:co-salient object detection,简称CoSOD。 翻译成:联合显著性目标分割。 我们看一个例子: 某Co-SOD模型的计算流程 模型的输入是一组图片,我们对这组图片进行联合注意力映射,得到联合注意力图Co-attention maps A,输入的每张图片都会得到一个A。
salient-object-detectionbackground-removalcamouflaged-object-detectiondichotomous-image-segmentationhigh-resolution-salient-object-detection UpdatedNov 18, 2024 Python jiwei0921/SOD-CNNs-based-code-summary- Star847 Code Issues Pull requests The summary of code and paper for salient object detection with dee...
作为一个重要的计算机视觉研究问题,近年显著目标检测(Salient Object Detection,SOD)吸引了越来越多研究者的关注。意料之中的是,显著目标检测的最新研究已经由深度学习方法所主导(deep SOD),多百篇该领域文章的发表予以了印证。为了促进对深度显著目标检测的理解,本文提供一个全面详尽的调查,涵盖多个算法的分类以及一些未...
它们也是《Salient Object Detection: A Survey》一文所主要关注的。这三篇(注意观察前两篇的版本)文章分别是: 1、T. Liu, J. Sun, N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,” in CVPR,2007, pp. 1–8....
《Salient Object Detection: A Survey》作者:Ali Borji、Ming-Ming Cheng、Huaizu Jiang and Jia Li 基本按照文章中文献出现的顺序。 一、L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE TPAMI, 1998. ...
A method and apparatus configured to identify a plurality of objects 2.1 in a captured video and/or audio scene, process the scene by removing an object 2.2 from the scene; measuring the effect of removing said object using received data 2.3; reintroducing said object into the scene 2.5; and...