Talking Papers Podcast
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Are you ready to explore the fascinating world of cutting-edge research in computer vision, machine learning, artificial intelligence, graphics, and beyond? Join us on this podcast by researchers, for researchers, as we venture into the heart of groundbreaking academic papers.
At Talking Papers, we've reimagined the way research is shared. In each episode, we engage in insightful discussions with the main authors of academic papers, offering you a unique opportunity to dive deep into the minds behind the innovation.
📚 Structure That Resembles a Paper 📝
Just like a well-structured research paper, each episode takes you on a journey through the academic landscape. We provide a concise TL;DR (abstract) to set the stage, followed by a thorough exploration of related work, approach, results, conclusions, and a peek into future work.
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Talking Papers Podcast
Jing Zhang - UC-Net
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
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🎥YouTube Channel: https://bit.ly/3eQOgwP
