Andrew Zisserman

Bio: Andrew Zisserman is an academic researcher from University of Oxford. The author has contributed to research in topics: Real image & Convolutional neural network. The author has an hindex of 167, co-authored 808 publications receiving 261717 citations. Previous affiliations of Andrew Zisserman include University of Edinburgh & Microsoft.

Measurement of Brain Structures Based on Statistical and Geometrical 3D Segmentation

Abstract: In this paper we present a novel method for three-dimensional segmentation and measurement of volumetric data based on the combination of statistical and geometrical information. We represent the shape of complex three-dimensional structures, such as the cortex by combining a discrete 3D simplex mesh with the construction of a smooth surface using triangular Gregory-Bezier patches. A Gaussian model for the tissues present in the image is adopted, and a classification procedure which also estimates and corrects for the bias field present in the MRI is used. Confidence bounds are produced for all the measurements, thus obtaining bounds on the position of the surface segmenting the image. Performance is illustrated on multiple sclerosis phantom data and on real data.

Oxford TRECVID 2006 - Notebook paper

Abstract: The Oxford team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, one using sparse and one using dense visual features to learn classifiers for all 39 required concepts, using the training data supplied by MediaMill [29] for the 2005 data. In addition, we also used a face specific classifier, with features computed for specific facial parts, to facilitate answering people-dependent queries such as “government leader”. We submitted 3 different runs for this task. OXVGG_A was the result of using the dense visual features only. OXVGG_OJ was the result of using the sparse visual features for all the concepts, except for “government leader”, “face” and “person”, where we prepended the results from the face classifier. OXVGG_AOJ was a run where we applied rank fusion to merge the outputs from the sparse and dense methods with weightings tuned to the training data, and also prepended the face results for “face”, “person” and “government leader”. In general, the sparse features tended to perform best on the more object based concepts, such as “US flag”, while the dense features performed slightly better on more scene based concepts, such as “military”. Overall, the fused run did the best with a Mean Average (inferred) Precision (MAP) of 0.093, the sparse run came second with a MAP of 0.080, followed by the dense run with a MAP of 0.053. For the interactive search task, we coupled the results generated during the high-level task with methods to facilitate efficient and productive interactive search. Our system allowed for several “expansion” methods based on the sparse and dense features, as well as a novel on the fly face classification system, which coupled a Google Images search with rapid Support Vector Machine (SVM) training and testing to return results containing a particular person within a few minutes. We submitted just one run, OXVGG_TVI, which performed well, winning two categories and coming above the median in 18 out of 24 queries. 1 High-level Feature Extraction Our approach here is to train an SVM for the concept in question, then score all key frames in the test set by the magnitude of their discriminant (the distance from the discriminating hyper-plane), and subsequently rank the test shots by the score of their keyframes. We have developed three methods for this task, each differing in their features and/or kernel. Two of the methods are applicable to general visual categories (such as airplane, mountain and road) and the third is specific to faces. The first two methods differ in that one uses sparse (based on region detectors) monochrome features, and the other uses dense (on a regular pixel grid) colour features. We now describe the three methods in some detail.

Self-supervised Multi-modal Alignment for Whole Body Medical Imaging

Abstract: This paper explores the use of self-supervised deep learning in medical imaging in cases where two scan modalities are available for the same subject. Specifically, we use a large publicly-available dataset of over 20,000 subjects from the UK Biobank with both whole body Dixon technique magnetic resonance (MR) scans and also dual-energy x-ray absorptiometry (DXA) scans. We make three contributions: (i) We introduce a multi-modal image-matching contrastive framework, that is able to learn to match different-modality scans of the same subject with high accuracy. (ii) Without any adaption, we show that the correspondences learnt during this contrastive training step can be used to perform automatic cross-modal scan registration in a completely unsupervised manner. (iii) Finally, we use these registrations to transfer segmentation maps from the DXA scans to the MR scans where they are used to train a network to segment anatomical regions without requiring ground-truth MR examples. To aid further research, our code is publicly available (https://github.com/rwindsor1/biobank-self-supervised-alignment).

Content-based image recognition on printed broadside ballads: The Bodleian Libraries' ImageMatch Tool

Abstract: This paper introduces the Bodleian Ballads ImageMatch tool, developed by the Visual Geometry Group of the University of Oxford’s Department of Software Engineering on behalf of the Bodleian Libraries. ImageMatch was designed to assist with the cataloguing and study of the pictorial content of early British printed broadside ballads, but has potential value for many other kinds of printed material. The paper outlines the nature of the materials to which ImageMatch has been applied; describes how the tool works and what it can do; and will offers some discussion on the benefits of ImageMatch’s for image-cataloguing in Rare Books collections.

LAEO-Net++: revisiting people Looking At Each Other in videos.

Abstract: Capturing the ‘mutual gaze’ of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos.

Deep Residual Learning for Image Recognition

Abstract: 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.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: 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

Abstract: 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.

ImageNet: A large-scale hierarchical image database

Abstract: 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.

U-Net: Convolutional Networks for Biomedical Image Segmentation

Abstract: 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 .