Talking Papers Podcast
Talking Papers Podcast: deep dives into research papers in computer vision, 3D, machine learning, and AI, with the authors who wrote them. Where research meets conversation. By researchers, for researchers.
Each episode is structured like the paper itself: a TL;DR / abstract to set the stage, then related work, approach, results, conclusions, and future work. We close with a bonus segment called "What did Reviewer 2 say?", where the authors share the candid peer-review story behind the publication.
Hosted by Itzik Ben-Shabat. Guests are PhD students, postdocs, and faculty from leading labs across academia and industry. Aimed at fellow researchers and graduate students who want the candid version of the work, not a polished press release.
Talking Papers Podcast
Jing Zhang - UC-Net
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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
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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
🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com
📧Subscribe to our mailing list: http://eepurl.com/hRznqb
🐦Follow us on Twitter: https://twitter.com/talking_papers
🎥YouTube Channel: https://bit.ly/3eQOgwP
