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.

/pdf/imagenet-a-large-scale-hierarchical-image-database-1e8f663s1h.pdf

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 .

/pdf/u-net-convolutional-networks-for-biomedical-image-2xoyjzggxv.pdf

U-Net: Convolutional Networks for Biomedical Image Segmentation

Multiple View Geometry in Computer Vision: Some Special Plane Projective Transformations

Author response: Diagnostically relevant facial gestalt information from ordinary photos

ObjectivesWe aimed to improve understanding of the role of imaging in diagnosis of low back pain by determining the prevalence of age-related disc degeneration in asymptomatic and symptomatic subjects. Spinal MRIs of symptomatic and asymptomatic subjects were re-annotated onto the same objective grading system and prevalence of degenerative changes compared. MethodsIn an exploratory cross-sectional study, we compared the prevalence of disc degeneration between two large groups of anonymised females, 30-80yrs, viz a symptomatic group with chronic back pain (724) and an asymptomatic (701) group. We used a verified automated MRI annotation system to re-annotate their spinal MRIs and report degeneration on the Pfirrmann (1-5) scale, and other degenerative changes (herniation, endplate defects, marrow signs, spinal stenosis) as binary present/absent. ResultsSevere degenerative changes were significantly more prevalent in discs of symptomatics than asymptomatics in the lower (L4-S1) but not the upper (L1-L3) lumbar discs in subjects <60years. We found high co-existence of several degenerative features in both populations. Degeneration was minimal in around 30% of symptomatics < 50years. ConclusionsAutomated MRI provides a valuable means of rapidly comparing large MRI datasets. Here, through directly comparing MRI annotations on the same objective scales it enabled us to detect significant age and spinal-level related differences in the prevalence of degenerative features between asymptomatic and symptomatic populations. By distinguishing between symptomatics whose discs have structural defects, and symptomatics with minimal degenerative changes, MRI could provide a means of clinical stratification, and provide a useful pathway to investigate possible pain sources. Key messagesO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIEven though intervertebral disc degeneration, and degenerative changes such as disc herniations, are strongly associated with low back pain, the importance of disc degeneration in development of low back pain is questioned because these degenerative changes are seen in both those with and those without low back pain; spinal MRIs are thus thought to be of little clinical value. C_LI What does this study add?O_LIThe study provides the first data on age-related degeneration in those without pain and shows the significant differences in prevalence between age-related and symptom-related disc degeneration. C_LIO_LIThe study provides definitive data showing that severe disc degeneration is directly implicated in a significant proportion of those with chronic low back pain, with the association with pain strongly dependent on age and spinal level C_LI How might this impact on clinical practice or future developments?O_LIThe study shows that even though severe disc degeneration is strongly associated with low back pain, 30% of younger (<50yrs) chronic low back pain patients have no evident disc degeneration detected by MRI, which is important information (currently not used) for clinicians in directing treatments (and perhaps a clearer reason for the proper use of scans). C_LIO_LIThe study provides important information for those working on mechanisms, as it enables stratification between pathways of pain arising from structural defects in the disc, and those pain pathways in discs with no such structural change. C_LI

https://medrxiv.org/content/10.1101/2021.11.08.21265571v1.full.pdf

Age and disc degeneration in low back pain: automated analysis enables a magnetic resonance imaging comparison of large cross-sectional cohorts of symptomatic and asymptomatic subjects.

I am delighted to introduce the first book on Multimedia Data Mining. When I came to know about this book project undertaken by two of the most active young researchers in the field, I was pleased that this book is coming in early stage of a field that will need it more than most fields do. In most emerging research fields, a book can play a significant role in bringing some maturity to the field. Research fields advance through research papers. In research papers, however, only a limited perspective could be provided about the field, its application potential, and the techniques required and already developed in the field. A book gives such a chance. I liked the idea that there will be a book that will try to unify the field by bringing in disparate topics already available in several papers that are not easy to find and understand. I was supportive of this book project even before I had seen any material on it. The project was a brilliant and a bold idea by two active researchers. Now that I have it on my screen, it appears to be even a better idea. Multimedia started gaining recognition in 1990s as a field. Processing, storage, communication, and capture and display technologies had advanced enough that researchers and technologists started building approaches to combine information in multiple types of signals such as audio, images, video, and text. Multimedia computing and communication techniques recognize correlated information in multiple sources as well as insufficiency of information in any individual source. By properly selecting sources to provide complementary information, such systems aspire, much like human perception system, to create a holistic picture of a situation using only partial information from separate sources. Data mining is a direct outgrowth of progress in data storage and processing speeds. When it became possible to store large volume of data and run different statistical computations to explore all possible and even unlikely correlations among data, the field of data mining was born. Data mining allowed people to hypothesize relationships among data entities and explore support for those. This field has been put to applications in many diverse domains and keeps getting more applications. In fact many new fields are direct outgrowth of data mining and it is likely to become a powerful computational tool.

Multiframe Super-Resolution from a Bayesian Perspective

A robot vehicle moving under visual guidance needs to compute approximate geometry of obstacles in its environment It is unreasonable to assume that egomotion is known to the sort of precision that is available for a camera mounted on a high quality robot arm Generally a nominal estimate for egomotion is available One possibility is to refine this estimate using optic flow data (Harris, 1987) Alternatively, the problem can be turned on its head: what geometric information remains stable under perturbation of assumed egomotion? This question has been addressed by Koenderink and van Doom (1977), Nelson and Aloimonos (1988) and Verri et al (1989), in the case of continuous motion fields and, in the domain of stereoscopic vision, by Weinshall (1990) Part of the answer, we claim, lies in the use of motion parallax as a geometric cue Motion parallax, which is a relative measure of the positions of two points, can be very much more robust as a cue than the absolute position of a single point This is true for computation of relative depth, curvature on specular surfaces and curvature on extremal boundaries

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Andrew Zisserman

Papers

Multiple View Geometry in Computer Vision: Some Special Plane Projective Transformations

Author response: Diagnostically relevant facial gestalt information from ordinary photos

Age and disc degeneration in low back pain: automated analysis enables a magnetic resonance imaging comparison of large cross-sectional cohorts of symptomatic and asymptomatic subjects.

Multiframe Super-Resolution from a Bayesian Perspective

Towards qualitative vision: motion parallax.