Machine Learning and Computer Vision Reading Group
This is a weekly seminar of our lab 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: Shuang WU and Li CHENG
Time and Location
- Thur. 16:00-17:30, Glycine Matrix
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.
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.
Jan. 19, 2017 -- Li Cheng
Title: Recurrent neural nets (RNNs) and its applications (slides)
Jan. 26, 2017 -- Shuang Wu
Paper: Yonghui Wu et al., Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. ICML, 2016.
Feb. 02, 2017 -- Yu Zhang
Paper: David Joseph Tan et al., Fits Like a Glove: Rapid and Reliable Hand Shape Personalization. CVPR, 2016.
Feb. 09, 2017 -- Chi Xu
Paper: Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, Jitendra Malik, Human Pose Estimation with Iterative Error Feedback. CVPR, 2016.
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.
Mar. 9, 2017 -- Lakshimi Govindarajan
Paper: Markus Oberweger, Paul Wohlhart, Vincent Lepetit, Training a feedback loop for hand pose estimation. ICCV, 2015.
Mar. 16, 2017 -- He Zhao
Paper: Nguyen A, Yosinski J, Bengio Y, et al., Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. arXiv:1612.00005, 2016.
Mar. 23, 2017 -- Shuang Wu
Paper: Oord A, Kalchbrenner N, Kavukcuoglu K, Pixel Recurrent Neural Networks. arXiv:1601.06759, 2016.
Mar. 30, 2017 -- Li Cheng
Online/Early Action Detection, Activity Forecasting and All That
Apr. 06, 2017 -- Yu Zhang
Paper: C Wan, T Probst, L Van Gool, A Yao, Crossing Nets: Dual Generative Models with a
Shared Latent Space for Hand Pose Estimation. arXiv:1702.03431, 2017.
Apr. 13, 2017 -- Chi Xu
Paper: Anastasia Tkach, Mark Pauly, Andrea Tagliasacchi, Sphere-Meshes for Real-Time Hand Modeling and Tracking, ACM Transactions on Graphics. Proceedings of SIGGRAPH Asia, 2016.
Apr. 20, 2017 -- Connie Kou
Paper: Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel, Value Iteration Networks. NIPS Proceedings, 2016.
May. 4, 2017 -- Yunchao Wei (Guest Speaker)
Title: Object Recognition with Image-level Annotation
Abstract: Among various levels of supervision information (e.g. labels, bounding boxes and pixel-level annotations), the simplest and most efficient one that can be collected for training is the image-level object category annotation. In this talk, he will introduce his recent efforts on multi-label classification, weakly-supervised object detection and weakly-supervised semantic segmentation. His works achieve some state-of-the-art results on these challenging tasks.
May. 11, 2017 -- He Zhao
Paper: Li R, Zeng T, Peng H, Ji S, Deep Learning Segmentation of Optical Microscopy Images Improves 3D Neuron Reconstruction. IEEE Transactions on Medical Imaging, 2017.
May 18, 2017 -- Lakshmi Govindarajan
Paper: Achal Dave, Olga Russakovsky, Deva Ramanan, Predictive-Corrective Networks for Action Detection. arXiv:1704.03615, 2017.
May 25, 2017 -- Satyam
Paper: Andre Esteva, Brett Kuprel et. al., Dermatologist-level classification of skin cancer with deep neural networks.. Nature, 2017.
June 1, 2017 -- Satyam
Paper: Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo, Deep Neural Decision Forests. ICCV, 2015.
- Reading Group 2016
- Reading Group 2015