Recently, numbers of works shows that the performance of neural machine translation (NMT) can be improved to a certain extent with using visual information. However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30K. In these kinds of datasets, the content of one bilingual parallel sentence pair must be well represented by a manually annotated image, which is different with the actual translation situation. Some previous works are proposed to addressed the problem by retrieving images from exiting sentence-image pairs with topic model. However, because of the limited collection of sentence-image pairs they used, their image retrieval method is difficult to deal with the out-of-vocabulary words, and can hardly prove that visual information enhance NMT rather than the co-occurrence of images and sentences. In this paper, we propose an open-vocabulary image retrieval methods to collect descriptive images for bilingual parallel corpus using image search engine. Next, we propose text-aware attentive visual encoder to filter incorrectly collected noise images. Experiment results on Multi30K and other two translation datasets show that our proposed method achieves significant improvements over strong baselines.

AdCreative.ai uses AI to build ad creatives for advertisers. With the effectiveness of AI and the human touch of real creatives, AdCreative.ai gives a revolutionary solution to an age-old problem…

To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods. One way to achieve this results is by exploiting approximate search techniques. In fact, when we deal with very large collections of data, using an exact search method makes the system very slow. In this project, we adopt the Locality Sensitive Hashing (LSH) index to implement a CBIR system that allows us to perform fast similarity search on deep features. Specifically, we exploit transfer learning techniques to extract deep features from images; this phase is done using two famous Convolutional Neural Networks (CNNs) as features extractors: Resnet50 and Resnet50v2, both pre-trained on ImageNet. Then we try out several fully connected deep neural networks, built on top of both of the previously mentioned CNNs in order to fine-tuned them on our dataset. In both of previous cases, we index the features within our LSH index implementation and within a sequential scan, to better understand how much the introduction of the index affects the results. Finally, we carry out a performance analysis: we evaluate the relevance of the result set, computing the mAP (mean Average Precision) value obtained during the different experiments with respect to the number of done comparison and varying the hyper-parameter values of the LSH index.

Researchers often spend weeks sifting through decades of unlabeled satellite imagery(on NASA Worldview) in order to develop datasets on which they can start conducting research. We developed an interactive, scalable and fast image similarity search engine (which can take one or more images as the query image) that automatically sifts through the unlabeled dataset reducing dataset generation time from weeks to minutes. In this work, we describe key components of the end to end pipeline. Our similarity search system was created to be able to identify similar images from a potentially petabyte scale database that are similar to an input image, and for this we had to break down each query image into its features, which were generated by a classification layer stripped CNN trained in a supervised manner. To store and search these features efficiently, we had to make several scalability improvements. To improve the speed, reduce the storage, and shrink memory requirements for embedding search, we add a fully connected layer to our CNN make all images into a 128 length vector before entering the classification layers. This helped us compress the size of our image features from 2048 (for ResNet, which was initially tried as our featurizer) to 128 for our new custom model. Additionally, we utilize existing approximate nearest neighbor search libraries to significantly speed up embedding search. Our system currently searches over our entire database of images at 5 seconds per query on a single virtual machine in the cloud. In the future, we would like to incorporate a SimCLR based featurizing model which could be trained without any labelling by a human (since the classification aspect of the model is irrelevant to this use case).

We are seeing more references to machine learning in how Google is ranking pages and other documents in search results. That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind. It's still worth considering some of those older ranking signals because they may play a role in how things are ranked. As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images. Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.

In Transfer learning, we would like to leverage the knowledge learned by a source task to help learning another target task. For example, a well-trained, rich image classification network could be leveraged for another image target related task. Another example, the knowledge learned by a network trained on simulated environment can be transferred to a network for the real environment. A well known example for transfer learning is to load the already trained large scale classification VGG network that is able to classify images into one of 1000 classes, and use it for another task such as classification of special medical images. Image search engines: Generally speaking, search engine usually takes a query and returns results.

Artificial intelligence is one of the fastest growing fields of computer science today and the demand for excellent AI Engineers is increasing day in and day out. This course will help you stay competitive in the AI job market by teaching you how to create a Deep Learning End-to-End product on your own. Most courses focus on the basics of Deep Learning and teach you about the very basics of different models. In this course, however, you will learn how to write a whole End-to-End pipeline, from data preprocessing across choosing the right hyper-parameters, to showing your users results in a browser. The case that we will tackle in this course is an engine for Image to Image Search.

A Russian search engine is being accused of providing an unregulated facial recognition system to members of the public -- violating personal privacy. Experts have slammed the feature as'poor' and'creepy' while dubbing it a'definite privacy concern'. Yandex, much like Google, Bing and other search engines, allows users to input an image and see similar results. But only Yandex, which claims to conduct more than 50 per cent of Russian searches on Android, produces images of the exact same person. MailOnline tested the image search facilities of Yandex, Bing, Google and specialist site TinEye by submitting a photo that was not available online.

You're interested in Computer Vision, Deep Learning, and OpenCV…but you don't know how to get started. Follow these steps to get OpenCV configured/installed on your system, learn the fundamentals of Computer Vision, and graduate to more advanced topics, including Deep Learning, Face Recognition, Object Detection, and more! Deep Learning algorithms are capable of obtaining unprecedented accuracy in Computer Vision tasks, including Image Classification, Object Detection, Segmentation, and more. Follow these steps and you'll have enough knowledge to start applying Deep Learning to your own projects. Using Computer Vision we can perform a variety of facial applications, including facial recognition, building a virtual makeover system (i.e., makeup, cosmetics, eyeglasses/sunglasses, etc.), or even aiding in law enforcement to help detect, recognize, and track criminals.

In this tutorial, you will learn how to build a scalable image hashing search engine using OpenCV, Python, and VP-Trees. Back in 2017, I wrote a tutorial on image hashing with OpenCV and Python (which is required reading for this tutorial). That guide showed you how to find identical/duplicate images in a given dataset. However, there was a scalability problem with that original tutorial -- namely that it did not scale! To find near-duplicate images, our original image hashing method would require us to perform a linear search, comparing the query hash to each individual image hash in our dataset. In a practical, real-world application that's far too slow -- we need to find a way to reduce that search to sub-linear time complexity. But how can we reduce search time so dramatically?