Machine Learning for Bioimage Analysis Reading Group
This is a seminar/reading group focused on recent trends in computer vision and machine learning. Each week one of our group members will present a new paper from venues including conferences
such as NIPS, ICML, ICCV, CVPR, and journals such as TPAMI, JMLR, IJCV. The seminar is open to all staff from BII. Researchers from other groups of BII are welcomed to attend or present at this seminar.
Organizer: Li Cheng
Time and Location
- Thur. 16:00-17:30, Glycine Matrix
Schedule
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Feb. 16, 2017 -- Liang Hui (guest speaker)
Title: Vision-based Hand Motion Analysis for Human-Computer Interaction
Abstract: We address the problem of hand pose and gesture analysis in RGB-D images with convolutional neural networks (CNN) and random forests. Both CNN and random forests are prevailing techniques for human hand and body pose analysis in recent years, while their accuracy is still unsatisfactory. In this talk we present two approaches based on CNN and random forest respectively with improved capability of handling ambiguous hand postures. The first is multi-view CNNs, in which a single depth image is projected to multiple planes, each of which is then sent to a CNN for pose regression and their results are fused to output robust prediction. The second is random forests with suppressed leaves, in which the leaf nodes are optimized to reflect their importance in Hough voting. Various real-time demos are also developed based on these methods to assist human computer interaction.
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Feb. 23, 2017 -- Lakshimi Govindarajan
Paper:
Previous Meetings
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Jan. 05, 2017 -- He Zhao
Paper: Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang and Russ Webb, Learning from Simulated and Unsupervised Images through Adversarial Training. arXiv:1612.07828v1, 2016.
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Jan. 12, 2017 -- Xiaowei Zhang
Paper: Tianfan Xue, Jiajun Wu, Katherine Bouman and Bill Freeman, Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks. NIPS, 2016.
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Jan. 19, 2017 -- Li Cheng
Title: Recurrent neural nets (RNNs) and its applications (slides)
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Jan. 26, 2017 -- Shuang Wu
Paper: Yonghui Wu et al., Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv:1609.08144v2, 2016.
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Feb. 02, 2017 -- Yu Zhang
Paper: David Joseph Tan et al., Fits Like a Glove: Rapid and Reliable Hand Shape Personalization. CVPR, 2016.
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Feb. 09, 2017 -- Chi Xu
Paper: Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, Jitendra Malik, Human Pose Estimation with Iterative Error Feedback. CVPR, 2016.
- Machine Learning for Bioimage Analysis Reading Group 2015
- Machine Learning for Bioimage Analysis Reading Group 2016