Our recent MIT-IBM research, presented at Neurips 2020, deals with hacker-proofing deep neural networks - in other words, improving their adversarial robustness. Our work on goal oriented captions is a step towards blind assistive technologies, and it opens the door to many interesting research questions that meet the needs of the visually impaired. If you think about it, there is seemingly no way to tell a bunch of numbers to come up with a caption for an image that accurately describes it. One application that has really caught the attention of many folks in the space of artificial intelligence is image captioning. The model employs techniques from computer vision and Natural Language Processing (NLP) to extract comprehensive textual information about … “Efficientdet: Scalable and efficient object detection”. The scarcity of data and contexts in this dataset renders the utility of systems trained on MS-COCO limited as an assistive technology for the visually impaired. The AI-powered image captioning model is an automated tool that generates concise and meaningful captions for prodigious volumes of images efficiently. This motivated the introduction of Vizwiz Challenges for captioning  images taken by people who are blind. [1] Vinyals, Oriol et al. It also makes designing a more accessible internet far more intuitive. Users have the freedom to explore each view with the reassurance that they can always access the best two-second clip … For full details, please check our winning presentation. Many of the Vizwiz images have text that is crucial to the goal and the task at hand of the blind person. Therefore, our machine learning pipelines need to be robust to those conditions and correct the angle of the image, while also providing the blind user a sensible caption despite not having ideal image conditions. Image captioning is a task that has witnessed massive improvement over the years due to the advancement in artificial intelligence and Microsoft’s algorithms state-of-the-art infrastructures. Here, it’s the COCO dataset. 9365–9374. This would help you grasp the topics in more depth and assist you in becoming a better Deep Learning practitioner.In this article, we will take a look at an interesting multi modal topic where w… [10] Steven J. Rennie et al. To address this, we use a Resnext network [3] that is pretrained on billions of Instagram images that are taken using phones,and we use a pretrained network [4] to correct the angles of the images. In: International Conference on Computer Vision (ICCV). [8] Piotr Bojanowski et al. Light and in-memory computing help AI achieve ultra-low latency, IBM-Stanford team’s solution of a longstanding problem could greatly boost AI, Preparing deep learning for the real world – on a wide scale, Research Unveils Innovations for IBM’s Cloud for Financial Services, Quantum Computing Education Must Reach a Diversity of Students. Automatic Captioning can help, make Google Image Search as good as Google Search, as then every image could be first converted into a caption … Firstly on accessibility, images taken by visually impaired people are captured using phones and may be blurry and flipped in terms of their orientations. Most image captioning approaches in the literature are based on a “Show and Tell: A Neural Image Caption Generator.” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), [2] Karpathy, Andrej, and Li Fei-Fei. Back in 2016, Google claimed that its AI systems could caption images with 94 percent accuracy. " [Image captioning] is one of the hardest problems in AI,” said Eric Boyd, CVP of Azure AI, in an interview with Engadget. Take up as much projects as you can, and try to do them on your own. arXiv: 1805.00932. In: CoRRabs/1603.06393 (2016). Automatic Image Captioning is the process by which we train a deep learning model to automatically assign metadata in the form of captions or keywords to a digital image. Unsupervised Image Captioning Yang Feng♯∗ Lin Ma♮† Wei Liu♮ Jiebo Luo♯ ♮Tencent AI Lab ♯University of Rochester {yfeng23,jluo}@cs.rochester.edu forest.linma@gmail.com wl2223@columbia.edu Abstract Deep neural networks have achieved great successes on Vizwiz Challenges datasets offer a great opportunity to us and the machine learning community at large, to reflect on accessibility issues and challenges in designing and building an assistive AI for the visually impaired. To ensure that vocabulary words coming from OCR and object detection are used, we incorporate a copy mechanism [9] in the transformer that allows it to choose between copying an out of vocabulary token or predicting an in vocabulary token. Given an image like the example below, our goal is to generate a caption such as "a surfer riding on a wave". In our winning image captioning system, we had to rethink the design of the system to take into account both accessibility and utility perspectives. In the paper “Adversarial Semantic Alignment for Improved Image Captions,” appearing at the 2019 Conference in Computer Vision and Pattern Recognition (CVPR), we – together with several other IBM Research AI colleagues — address three main challenges in bridging … Try it for free. In: Transactions of the Association for Computational Linguistics5 (2017), pp. Microsoft has developed an image-captioning system that is more accurate than humans. To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption. app developers through the Computer Vision API in Azure Cognitive Services, and will start rolling out in Microsoft Word, Outlook, and PowerPoint later this year. When you have to shoot, shoot You focus on shooting, we help with the captions. Partnering with non-profits and social enterprises, IBM Researchers and student fellows since 2016 have used science and technology to tackle issues including poverty, hunger, health, education, and inequalities of various sorts. “Ideally, everyone would include alt text for all images in documents, on the web, in social media – as this enables people who are blind to access the content and participate in the conversation,” said Saqib Shaikh, a software engineering manager at Microsoft’s AI platform group. 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