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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 BarnesABSTRACT: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.💻SUBSCRIBE AND FOLLOW:🎧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/3eQOgwPCODE:💻https://github.com/JingZhang617/UCNetRELATED PAPERS:📚A probabilistic u-net for segmentation of ambiguous images 📚Learning structured output representation using deep conditional generative modelsCONTACT:-----------------If you would like to be a guest, sponsor or just share your thoughts, feel free to reach out via email: [email protected] STAMPS-----------------------00:00 | 00:02 | Intro00:31 | The Authors01:07 | Abstract / TLDR02:41 | Motivation07:18 | Related Work09:20 | Approach18:32 | Results24:04 | Conclusions and future work25:42 | What did reviewer 2 say?29:49 | Outro#talkingpapers #CVPR2020 #RGBDSaliency#machinelearning #deeplearning #AI #neuralnetworks #research #computervision #artificialintelligence
