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Multimedia Laboratory, CUHK
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PolyNet: A Pursuit of Structural Diversity in Very Deep Networks

https://arxiv.org/abs/1611.05725

We explore structural diversity in designing deep networks, a new dimension beyond just depth and width. Specifically, we present a new family of modules, namely the PolyInception, which can be flexibly inserted in isolation or in a composition as replacements of different parts of a network. Choosing PolyInception modules with the guidance of architectural efficiency can improve the expressive power while preserving comparable computational cost. A benchmark on the ILSVRC 2012 validation set demonstrates substantial improvements over the state-of-the-art. Compared to Inception-ResNet-v2, it reduces the top-5 error on single crops from 4.9% to 4.25%, and that on multi-crops from 3.7% to 3.45%.
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PolyNet: A Pursuit of Structural Diversity in Very Deep Networks

https://arxiv.org/abs/1611.05725

We explore structural diversity in designing deep networks, a new dimension beyond just depth and width. Specifically, we present a new family of modules, namely the PolyInception, which can be flexibly inserted in isolation or in a composition as replacements of different parts of a network. Choosing PolyInception modules with the guidance of architectural efficiency can improve the expressive power while preserving comparable computational cost. A benchmark on the ILSVRC 2012 validation set demonstrates substantial improvements over the state-of-the-art. Compared to Inception-ResNet-v2, it reduces the top-5 error on single crops from 4.9% to 4.25%, and that on multi-crops from 3.7% to 3.45%.
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Chao Dong, Chen Change Loy, Xiaoou Tang, Accelerating the Super-Resolution Convolutional Neural Network, ECCV 2016

Project Page and Codes
http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html



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New state-of-the-art action recognition with a framework called temporal segment networks.

Paper:
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, L. Van Gool,
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition, ECCV 2016

Arxiv Preprint:
http://arxiv.org/abs/1608.00859v1

Code & Models
https://github.com/yjxiong/temporal-segment-networks
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Fashion landmark is a more discriminative representation than human joints and bounding boxes
to understand fashion images. A large-scale fashion landmark detection benchmark will be released together with the DeepFashion database.

Paper:
Ziwei Liu, Sijie Yan, Ping Luo, Xiaogang Wang, and Xiaoou Tang, Fashion Landmark Detection in the Wild, ECCV 2016

DeepFashion:
http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html

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S. Zhu, S. Liu, C. C. Loy, X. Tang, Deep Cascaded Bi-Network for Face Hallucination, ECCV 2016

Paper:
http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2016_hallucination.pdf

Technical Report:
http://arxiv.org/abs/1607.05046

Code:
https://github.com/zhusz/ECCV16-CBN
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Learning Deep Representation for Imbalanced Classification

Project Page and Code:
http://mmlab.ie.cuhk.edu.hk/projects/LMLE.html



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Slicing Convolutional Neural Network for Crowd Video Understanding

Project Page:
http://www.ee.cuhk.edu.hk/~jshao/SCNN.html

Video:
https://www.youtube.com/watch?v=TQRTtxK_RA0

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