Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

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Deep Residual Learning for Image Recognition

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Very Deep Convolutional Networks for Large-Scale Image Recognition

The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.

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ImageNet: A large-scale hierarchical image database

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

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U-Net: Convolutional Networks for Biomedical Image Segmentation

IEEE Computer Society Conference on Computer Vision and Pattern Recognition

The papers in this special section were presented at the CVPR 2015 conference that was held in Boston, MA.

Guest Editorial: Best of CVPR 2015

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Constrained Video Face Clustering using1NN Relations.

공간 트랜스포머 모듈을 포함하는 이미지 프로세싱 신경 네트워크 시스템을 이용하여 입력들을 프로세싱하기 위한, 컴퓨터 저장 매체 상에 인코딩된 컴퓨터 프로그램들을 포함하여 방법들, 시스템들 및 장치들이 제공된다. 방법들 중 하나는 하나 이상의 입력 이미지들로부터 도출된 입력 특징 맵을 수신하는 단계와 그리고 트랜스폼된 특징 맵을 생성하기 위해 상기 입력 특징 맵에 공간 트랜스포메이션을 적용하는 단계를 포함하고, 상기 적용하는 단계는 상기 공간 트랜스포메이션을 위한 공간 트랜스포메이션 파라미터들을 생성하기 위해 상기 입력 특징 맵을 프로세싱하는 것과 그리고 상기 트랜스폼된 특징 맵을 생성하기 위해 상기 공간 트랜스포메이션 파라미터들에 따라 상기 입력 특징 맵으로부터 샘플링하는 것을 포함한다.

Andrew Zisserman

Papers

IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Guest Editorial: Best of CVPR 2015

Constrained Video Face Clustering using1NN Relations.

공간 트랜스포머 모듈들

Introduction and Chapter Summary