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See live examples of some of the awesome work we've been doing in Computer Vision and NLP.
All our demos are built keeping high impact, real world use cases in mind.
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Our model assigns a class to every pixel in an image you upload allowing you to crisply outine objects present in images. Some cool use cases including area delineation from aerial imagery, processing medical imagery, and enhancning pictures.
Ever wondered how much you resemble Leonardo Di Caprio? Our model predicts similarity in faces using Deep Learning. A strong real world use case is searching large volumes of video footage for specific people.
Our model yields high accuracies for a wide variety of Object Detection tasks. The model can be trained on almost any custom object you want to detect.
Be it exercise, sports, or aerobics, pose estimation models have proven to be useful for form correction. Interesting use cases include fall detection, form correction, and avatar substitution.
Image Super Resolution
We all have wanted lossless resizing of images in the past. Neural networks have evolved to perform well on super resolution tasks. Super resolution can be applied in image editing, better performance in facial recognition, and video compression.
Sentiment Analysis has been a vanilla NLP task for some years now and finds use cases in news analysis, analyzing product reviews, call transcripts, and social media analytics.
What if you could have a machine digest huge volumes of text and answer your questions without you having to manually go through everything? Automated question answering is creating a paradigm shift in all research oriented tasks across multiple industries.
Generative Language Models can now sift through large instances of text data, pay attention to important elements, and summarize crisp insights in their own words. Summarization has a huge potential to disrupt the status quo in industries like Finance, Pharmaceuticals, Advertising, Law, and Media.
Having a machine understand semantic similarity between documents leads to interesting use cases. Search engines for custom business documents, high recall plagiarism detection, imputing training data, and identifying common themes in reports are some high value applications.
Named Entity Recognition
NER has been used to extract valuable information on entities (people, geographies, companies, and dates) from text data like news articles, reports, and web pages. An interesting add-on includes building Knowledge Graphs to capture structured, high value information.