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20180801-23 李伏欣:Some Understandings and New Designs of Recurrent and

2018-7-26 17:30| 发布者: 程一-计算所| 查看: 357| 评论: 0

摘要: 报告嘉宾:李伏欣(Corvallis, OR USA)报告时间:2018年08月01日(星期三)上午10:00(北京时间)报告题目:Some Understandings and New Designs of Recurrent and Convolutional Network Structures主持人:杨猛 ...

报告嘉宾:李伏欣Corvallis, OR USA

报告时间:2018年08月01日(星期三)上午10:00(北京时间)

报告题目:Some Understandings and New Designs of Recurrent and Convolutional Network Structures

主持人:杨猛中山大学


报告人简介:

Fuxin Li is currently an assistant professor in the School of Electrical Engineering and Computer Science at Oregon State University. Before that, he has held research positions in University of Bonn and Georgia Institute of Technology. He had obtained a Ph.D. degree in the Institute of Automation, Chinese Academy of Sciences in 2009. He (co-)won the PASCAL VOC semantic segmentation challenges from 2009-2012, and led a team to the 4th place finish in the DAVIS Video Segmentation challenge 2017. He has published more than 40 papers in computer vision, machine learning and natural language processing. His main research interests are deep learning, video object segmentation, multi-target tracking, structural prediction and human understanding of deep learning.


个人主页:

http://web.engr.oregonstate.edu/~lif/


报告摘要:

In this talk, I will talk about some of our recent work in understanding and reforming the well-known LSTM and CNN architectures. In the first part, I will talk about our experience utilizing LSTM in multi-target tracking and show some intuitions about why the current LSTM may be insufficient for long-term multi-object tracking. A novel bilinear LSTM model suitable for multi-target tracking problems will be proposed, motivated by the classic recursive least squares formulation. Results on the MOT 2016 and MOT 2017 challenges will be shown that significantly outperform traditional LSTMs in terms of identity switches. In the second part, I will talk about several of our recent work in understanding CNN from different angles, including a Gaussian complexity theory for understanding the generalization capabilities of the CNN and re-designing it for specific non-natural image problem domains, the explanation neural network (XNN) for generating human-understandable visual explanations of deep network decisions, and the PointConv approach we recently developed for implementing CNN on irregular point cloud data.


参考文献:

[1] Chanho Kim, Fuxin Li, James M. Rehg. Multiple Hypothesis Tracking Revisited. ICCV 2015.

[2] Chanho Kim, Fuxin Li, James M. Rehg. Multi-object Tracking with Neural Gating using bilinear LSTMs. ECCV 2018.

[3] Xingyi Li, Fuxin Li, Xiaoli Fern, Raviv Raich. Filter Shaping for Convolutional Networks. ICLR 2017.

[4] Zhongang Qi, Saeed Khorram, Fuxin Li. Embedding Neural Networks into Visual Explanations. arXiv:1709.05360 (preliminary version published on NIPS 2017 workshop on Interpreting, Explaining and Visualizing Deep Learning - Now what?)

[5] Wenxuan Wu, Zhongang Qi, Fuxin Li. PointConv: Deep Convolutional Networks on 3D Point Clouds. To appear on arXiv by 7/31.


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特别鸣谢本次Webinar主要组织者:

VOOC责任委员:杨猛(中山大学)

VODB协调理事:王瑞平(中科院计算所


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