We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

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ImageNet Classification with Deep Convolutional Neural Networks

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

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

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Going deeper with convolutions

Shared virtual memory is a software technique to provide shared memory on a network of computers without special hardware support. Although several relaxed consistency models and implementations are quite effective, there is still a considerable performance gap between the "software-only" approach and the hardware approach that uses directory-based caches. Automatic update is a simple communication mechanism, implemented in the SHRIMP multicomputer, that forwards local writes to remote memory transparently. In this paper we propose a new lazy release consistency based protocol, called Automatic Update Release Consistency (AURC), that uses automatic update to propagate and merge shared memory modifications. We compare the performance of this protocol against a software-only LRC implementation on several Splash-2 applications and show that the AURC approach can substantially improve the performance of LRC. For 16 processors, the average speedup has increased from 5.9 under LRC to 8.3 under AURC.

Improving release-consistent shared virtual memory using automatic update

Efficient similarity search in high-dimensional spaces is important to content-based retrieval systems. Recent studies have shown that sketches can effectively approximate L1 distance in high-dimensional spaces, and that filtering with sketches can speed up similarity search by an order of magnitude. It is a challenge to further reduce the size of sketches, which are already compact, without compromising accuracy of distance estimation.This paper presents an efficient sketch algorithm for similarity search with L2 distances and a novel asymmetric distance estimation technique. Our new asymmetric estimator takes advantage of the original feature vector of the query to boost the distance estimation accuracy. We also apply this asymmetric method to existing sketches for cosine similarity and L1 distance. Evaluations with datasets extracted from images and telephone records show that our L2 sketch outperforms existing methods, and the asymmetric estimators consistently improve the accuracy of different sketch methods. To achieve the same search quality, asymmetric estimators can reduce the sketch size by 10% to 40%.

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Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces

A scalable, high-resolution display may be constructed by tiling many projected images over a single display surface. One fundamental challenge for such a display is to avoid visible seams due to misalignment among the projectors. Traditional methods for avoiding seams involve sophisticated mechanical devices and expensive CRT projectors, coupled with extensive human effort for fine-tuning the projectors. The paper describes an automatic alignment method that relies on an inexpensive, uncalibrated camera to measure the relative mismatches between neighboring projectors, and then correct the projected imagery to avoid seams without significant human effort.

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Automatic alignment of high-resolution multi-projector display using an un-calibrated camera

The recent theoretical advances on compact data structures (also called "sketches") have raised the question of whether they can effectively be applied to content-based image retrieval systems. The main challenge is to derive an algorithm that achieves high-quality similarity searches while using compact metadata. This paper proposes a new similarity search method consisting of three parts. The first is a new region feature representation with weighted $=1 distance function, and EMD* match, an improved EMD match, to compute image similarity. The second is a thresholding and transformation algorithm to convert feature vectors into very compact data structures. The third is an EMD embedding based filtering method to speed up the query process. We have implemented a prototype system with the proposed method and performed experiments with a 10,000 image database. Our results show that the proposed method can achieve more effective similarity searches than previous approaches with metadata 3 to 72 times more compact than previous systems. The experiments also show that our EMD embedding based filtering technique can speed up the query process by a factor of 5 or more with little loss in query effectiveness.

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Kai Li

Papers

Improving release-consistent shared virtual memory using automatic update

Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces

Automatic alignment of high-resolution multi-projector display using an un-calibrated camera

Image similarity search with compact data structures

A trace-driven comparison of algorithms for parallel prefetching and caching