Computer vision technology has revolutionized how machines interact with their environment. By developing algorithms that can analyze and make informed decisions from visual data, this technology enhances machine perception, opening up new possibilities for enterprise applications. The scope of computer vision at Inblik Research spans several key areas, each contributing to the broader goal of enabling machines to understand and interact with the visual world more effectively..
Visual Understanding and Classification
Depth Estimation & Image Classification: Depth estimation is critical in understanding the 3D structure from 2D images, a technology pivotal in areas like autonomous driving and augmented reality, where spatial awareness is key. Image classification, on the other hand, involves categorizing images into predefined classes, playing a crucial role in organizing large datasets, aiding in medical diagnostics, and enhancing search functionalities.
Video classification extends the principles of image classification to dynamic scenes, essential in monitoring and analyzing content for various applications such as media, security, and sports. Zero-shot image classification takes this a step further by enabling the recognition of objects or scenes that the model has not encountered during training, making it invaluable in fields where encountering novel or rare objects is common.
Object Recognition and Interaction
Object Detection & Zero-Shot Object Detection: Object detection is fundamental in determining both the presence and position of objects within images, crucial for applications in surveillance, retail, and manufacturing. The ability to detect objects that the model hasn't been explicitly trained on, known as zero-shot object detection, adds a layer of adaptability and intelligence, making the technology suitable for ever-changing environments like urban settings.
Mask Generation: This technology is extensively used in the realm of image editing and augmentation. It's particularly vital in applications such as virtual try-on technology for clothes or makeup, and in the generation of synthetic training data for other computer vision tasks.
Image Analysis and Segmentation
Image Segmentation: This technique divides an image into multiple segments or parts, simplifying the representation of the image into more meaningful and easier-to-analyze segments. It’s widely used in medical imaging for identifying specific regions, in autonomous vehicles for better understanding road scenes, and in agricultural technology for detailed crop analysis.
Unconditional Image Generation: The generation of images from scratch, without any conditional inputs, is a breakthrough in creative AI. This finds applications in fields like design, fashion, and entertainment, and is also instrumental in generating diverse datasets for training other AI models.
Image Transformation and Translation
Image-to-Image Translation: This process involves converting an image from one type of representation to another. This has significant applications in artistic image generation, photo editing, and even in scientific visualizations where converting data into a more understandable format is essential.
Zero-Shot Object Detection: It involves the ability of AI models to detect and identify objects that they have not been explicitly trained to recognize. This capability is particularly valuable in dynamic and unpredictable environments, where encountering new and unknown objects is common.
