Dual Path Network and Its Applications

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Transcription:

Learning and Vision Group (NUS), ILSVRC 2017 - CLS-LOC & DET tasks Dual Path Network and Its Applications National University of Singapore: Yunpeng Chen, Jianan Li, Huaxin Xiao, Jianshu Li, Xuecheng Nie, Xiaojie Jin, Jiashi Feng Qihoo 360 AI Institute: Jian Dong, Shuicheng Yan Speaker: Jianshu Li Thank Min Lin, Qiang Chen from Qihoo 360 for the extensive discussions. Thank Xiaoli Liu, Ying Liu from Qihoo 360 for helping collect and annotate "external" data.

Results Overview Object Localization a) with "provided" data: 1 st place ( Loc Error: 6.23% ) b) with "external" data: 1 st place ( Loc Error: 6.19% ) Object Detection a) with "provided" data: 2 nd place ( by map: 65.8% ) b) with "external" data: 2 nd place ( by map: 65.8% ) Object Detection from video (VID) a) with "provided" data: 2 nd place ( by map: 75.8% ) b) with "external" data: 2 nd place ( by map: 76.0% )

Dual Path Networks (DPN) https://arxiv.org/abs/1707.01629 https://github.com/cypw/dpns

Motivation [1] * Here, a 1 1 convolutional layer (underlined) is added for consistency with the micro-block design in (a). [1]: G Huang, et al. "Densely Connected Convolutional Networks". CVPR 2017

Motivation [1] [1]: G Huang, et al. "Densely Connected Convolutional Networks". CVPR 2017

Analysis Share Info....... Cross-layer parameter sharing

Analysis When green arrows share parameters, they produce exactly the same outputs. Share Info.......

Analysis When green arrows share parameters, they produce exactly the same outputs. Thus, some computations are redundant here. Share Info.......

Analysis When green arrows share parameters, they produce exactly the same outputs. Thus, some computations are redundant here. We can add a new path to temporarily Share save Info the outputs of green arrows for reuse, and only execute the operation in the orange arrows........

Analysis A new path to temporarily save the outputs from the green arrows for reuse. Simplify (c) Densely Connected Network ( with shared connections )

Analysis A new path to temporarily save the outputs from the green arrows for reuse. A residual unit (c) Densely Connected Network ( with shared connections )

A new path to temporarily save the outputs from the green arrows for reuse. Analysis ( Residual Path ) (c) Densely Connected Network ( with shared connections )

Analysis Cross-layer parameter sharing

Analysis Residual Networks are essentially Densely Connected Networks but with shared connections. DenseNets ResNets

Analysis Residual Networks are essentially Densely Connected Networks but with shared connections. DenseNets ResNets Advantage: - ResNet: features refinement (reuse feature) - DenseNet: keep exploring new features

Dual Path Architecture Senior Employees Freshman When managing a company: Employees need to keep improving the skills (Feature refinement) Also need to hire freshman to the company (Feature exploration)

~ ~ Dual Path Architecture (d) Dual Path Architecture

~ ~ Dual Path Architecture Explore New Features (d) Dual Path Architecture Feature Refinement (Reuse Features)

~ ~ Dual Path Networks (e) DPN

~ ~ Dual Path Networks The sub-network can be replaced by any micro-structures, not necessarily a bottleneck structure (e) DPN

~ ~ Dual Path Networks Three DPNs are designed: DPN-92, DPN-98, DPN-131 depth=92 depth=98 depth=131 (e) DPN

~ ~ Dual Path Networks Three DPNs are designed: DPN-92, DPN-98, DPN-131 depth=92 depth=98 depth=131 ResNeXt-101 (64x4d) [2] DPN-98 320 MB Model Size 236 MB - 26% 15.5 GFLOPs 11.7-25% 12.1 GB 20.4 / 5.3 GPU Memory Top 1 / Top 5 Error. 11.1 GB 20.2 / 5.2-8% (e) DPN * Testing scale: x224 / Batch Size: 32 per GPU [2]: S Xie, et al. "Aggregated residual transformations for deep neural networks." CVPR. 2017.

Performance Single model, Single center-crop, Top-5 val error rate 4.4% 4.25% 4.16% DPN-131 is Fast!! Training = 60 img/sec (per node, 4 x K80 cards) ResNeXt-101 (64x4d) [2] Very Deep PolyNet [3] DPN-131 ( Best single model reported ) *Testing scale: x299 / x320 [3] X Zhang, et al. "Polynet: A pursuit of structural diversity in very deep networks" arxiv 2016

ILSVRC 2017

ILSVRC 2017: Object Localization & Detection Main Framwork: Input Image Ensembled CLS Models DPN-92, DPN-98, DPN-131 ResNeXt-101(64x4d), CRU-Net, DenseNet,... Multi-sacle Dense Testing Weighted Sum Confidence of each Class Input Image Ensembled Faster R-CNNs[4] Loc/Det Results Simplest Faster R-CNN Pipleline Weighted Sum DPN-92, DPN-98, DPN-131 [4] S Ren, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." NIPS. 2015

ILSVRC 2017: Object Localization Visualization:

ILSVRC 2017: Object Detection Visualization:

Thank You! National University of Singapore: Yunpeng Chen, Jianan Li, Huaxin Xiao, Jianshu Li, Xuecheng Nie, Xiaojie Jin, Jiashi Feng Qihoo 360 AI Institute: elefjia@nus.edu.sg Jian Dong, Shuicheng Yan yanshuicheng@360.cn

Q & A

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