Talking Papers Podcast

Jing Zhang - UC-Net

January 20, 2022 Itzik Ben-Shabat Season 1 Episode 3
Talking Papers Podcast
Jing Zhang - UC-Net
Show Notes Chapter Markers

PAPER TITLE:
"UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders"

AUTHORS:
Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Sadat Saleh, Tong Zhang, Nick Barnes

ABSTRACT:
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and produce a single saliency map following a deterministic learning pipeline. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space. With the proposed saliency consensus process, we are able to generate an accurate saliency map based on these multiple predictions. Quantitative and qualitative evaluations on six challenging benchmark datasets against 18 competing algorithms demonstrate the effectiveness of our approach in learning the distribution of saliency maps, leading to a new state-of-the-art in RGB-D saliency detection.

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CODE:
💻https://github.com/JingZhang617/UCNet

RELATED PAPERS:
📚A probabilistic u-net for segmentation of ambiguous images
📚Learning structured output representation using deep conditional generative models

CONTACT:
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TIME STAMPS
-----------------------
00:00 |  
00:02 |  Intro
00:31 |  The Authors
01:07 |  Abstract / TLDR
02:41 |  Motivation
07:18 |  Related Work
09:20 |  Approach
18:32 |  Results
24:04 |  Conclusions and future work
25:42 |  What did reviewer 2 say?
29:49 |  Outro

#talkingpapers #CVPR2020 #RGBDSaliency
#machinelearning #deeplearning #AI #neuralnetworks #research #computervision #artificialintelligence

Intro
Authors
Abstract
Motivation
Related Work
Approach
Results
Conclusions and future work
What did reviewer 2 say?