ELASTIC: Improving CNNs With Dynamic Scaling Policies

Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have a similar theme: a set of intuitive and manually designed policies that are generic and fixed (e.g. SIFT or feature pyramid). We argue that the scaling policy should be learned from data. In this paper, we introduce Elastic, a simple, efficient and yet very effective approach to learn a dynamic scale policy from data. We formulate the scaling policy as a non-linear function inside the network's structure that (a) is learned from data, (b) is instance specific, (c) does not add extra computation, and (d) can be applied on any network architecture. We applied Elastic to several state-of-the-art network architectures and showed consistent improvement without extra (sometimes even lower) computation on ImageNet classification, MSCOCO multi-label classification, and PASCAL VOC semantic segmentation. Our results show major improvement for images with scale challenges. Our code is available here: https://github.com/allenai/elastic

[1]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[5]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[6]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Linda G. Shapiro,et al.  ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation , 2018, ECCV.

[8]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[9]  Wei Liu,et al.  ParseNet: Looking Wider to See Better , 2015, ArXiv.

[10]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[12]  Jianxin Wu,et al.  Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  Kilian Q. Weinberger,et al.  CondenseNet: An Efficient DenseNet Using Learned Group Convolutions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Qi Wu,et al.  Multilabel Image Classification With Regional Latent Semantic Dependencies , 2016, IEEE Transactions on Multimedia.

[15]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[16]  Yizhou Yu,et al.  Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Rogério Schmidt Feris,et al.  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.

[18]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Trevor Darrell,et al.  Deep Layer Aggregation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[23]  Jitendra Malik,et al.  Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Ronan Collobert,et al.  Learning to Refine Object Segments , 2016, ECCV.

[25]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[26]  T. Lindeberg,et al.  Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales , 1994 .

[27]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Xiaonan Luo,et al.  Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks , 2017, AAAI.

[29]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[30]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[31]  Fuchun Sun,et al.  HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Yale Song,et al.  Improving Pairwise Ranking for Multi-label Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[35]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[37]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[38]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Thomas A. Funkhouser,et al.  Dilated Residual Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Sina Honari,et al.  Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Nenghai Yu,et al.  Learning Spatial Regularization with Image-Level Supervisions for Multi-label Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).