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Case Studies
Capabilities
- Video/Image ... Vision ... Colorize ... Image/Video Transfer Learning
- End-to-End Speech ... Synthesize Speech ... Speech Recognition
Predict image
Screening; Passenger, Luggage, & Cargo
DeepLens - deep learning enabled video camera
- Polly
Rekognition Video
Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI
Image Classification
Image-to-Image Translation
Landing AI ... LandingLens™, an enterprise AIOps/MLOps platform that offers to build, iterate, and operationalize AI powered visual inspection solutions for manufacturers
Object Detection Using Convolutional Neural Networks | The Straight Dope
Object detection with neural networks — a simple tutorial using keras | Johannes Rieke - Towards Data Science
Object Localization and Detection | leonardoaraujosantos
Why Tesla Invented A New Neural Network | Ambika Choudhury - Analytics India Magazine
Introducing Segment Anything: Working toward the first foundation model for image segmentation | Meta AI ... identifying which image pixels belong to an object

Computer vision involves developing algorithms and techniques to enable computers to interpret, analyze, and understand visual data from the world around them. It focuses on developing methods for computers to perceive, analyze, and interpret digital images or videos in a way that simulates human vision. Computer vision involves tasks such as object detection, image recognition, segmentation, tracking, and scene reconstruction. It is used in a wide range of applications, including robotics, autonomous vehicles, security and surveillance, medical imaging, and augmented reality.

Vision Transformers (ViT)

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ViT is a transformer that is targeted at vision processing tasks such as image recognition. It was first proposed in 2019 by Cordonnier et al. and later empirically evaluated more extensively in the well-known paper "An image is worth 16x16 words". ViT works by breaking down input images into a series of patches which, once transformed into vectors, are seen as words in a normal transformer. Each image is split into a sequence of fixed-size non-overlapping patches, which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image, which can be used for classification. The authors also add absolute position embeddings and feed the resulting sequence of vectors to a standard Transformer encoder. A [CLS] token is a special token that is used in classification tasks. It stands for “classification” and is used as the only input of the final MLP Head as it has been influenced by all the others. In the case of ViT, it is added to serve as representation of an entire image. The final MLP Head refers to the final Multi-Layer Perceptron (MLP) layer in the model. It takes the [CLS] token as input and outputs the final classification result.