Computer Vision

The intersection of artificial intelligence and multimedia continues to evolve, breaking down barriers between different forms of media. In this project, the research titled “Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators” conducted by Picsart AI Research Lab represents a significant breakthrough. This study introduces a pioneering method that directly converts textual descriptions into videos, bridging the gap between natural language processing and computer vision. This development not only caters to the growing demand for dynamic visual content but also showcases the machine’s capability to interpret and transform human language into a visual format. By addressing the challenge of text-to-video synthesis, this research sets a new standard for interdisciplinary studies in artificial intelligence. ...

Special thanks to Mega Satish and Hasan Rizvi for their meaningful contributions, support, and wisdom that helped shape this work. We propose to develop a program that can show a QuadTree view and data model architecture. Nowadays, many digital map applications have the need to present large quantities of precise point data on the map.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. Deep learning’s breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results.

Special thanks to Archit Konde for his meaningful contributions, support, and wisdom that helped shape this work. The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs).

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. This paper aims to demonstrate the efficiency of the Adversarial Open Domain Adaption framework for sketch-to-photo synthesis. The unsupervised open domain adaption for generating realistic photos from a hand-drawn sketch is challenging as there is no such sketch of that class for training data.

Special thanks to Mega Satish and Hasan Rizvi for their meaningful contributions, support, and wisdom that helped shape this work. In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos.