Building Maximal Vision Systems with Minimal Resources

Dr. Ayush Bansal

Date : 19/12/2022

Abstract:

Current vision and robotic systems are like mainframe machines of the 60s -- they require extensive resources: (1) dense data capture and massive human annotations, (2) large parametric models, and (3) intensive computational infrastructure. I build systems that can learn directly from sparse and unconstrained real-world samples with minimal resources, i.e., limited or no supervision, use simple and efficient models, and operate on every day computational devices. Building systems with minimal resources allows us to democratize them for non-experts. My work has impacted important areas such as virtual reality, content creation and audio-visual editing, and providing a natural voice to speech-impaired individuals.

In my talk, I will present my efforts to build vision systems for novel view synthesis. I will discuss Neural Pixel Composition, a novel approach for continuous 3D-4D view synthesis that reliably operates on sparse and wide-baseline multi-view images/videos and can be trained efficiently within a few minutes for high-resolution (12MP) content using 1 GB GPU memory. I will present my efforts to build vision systems for unsupervised audio-visual synthesis. I will primarily discuss Exemplar Autoencoders that enable zero-shot audio-visual retargeting. Exemplar Autoencoders are built on remarkably simple insights: (1) autoencoders project out-of-sample data onto the distribution of the training set; and (2) exemplar learning enables us to capture the voice, stylistic prosody (emotions and ambiance), and visual appearance of the target. These properties enable an autoencoder trained on an individual's voice to generalize for unknown voices in different languages. Exemplar Autoencoders can synthesize natural voices for speech-impaired individuals and do a zero-shot multilingual translation.

Towards Autonomous Driving in Dense, Heterogeneous, and Unstructured Environments

Dr. Rohan Chandra

Date : 07/11/2022

Abstract:

In this talk, I discuss many key problems in autonomous driving towards handling dense, heterogeneous, and unstructured traffic environments. Autonomous vehicles (AV) at present are restricted to operating on smooth and well-marked roads, in sparse traffic, and among well-behaved drivers. I present new techniques to perceive, predict, and navigate among human drivers in traffic that is significantly denser in terms of a number of traffic-agents, more heterogeneous in terms of size and dynamic constraints of traffic agents, and where many drivers may not follow the traffic rules and have varying behaviors. My talk is structured along three themes—perception, driver behavior modeling, and planning. More specifically, I will talk about Improved tracking and trajectory prediction algorithms for dense and heterogeneous traffic using a combination of computer vision and deep learning techniques. A novel behavior modeling approach using graph theory for characterizing human drivers as aggressive or conservative from their trajectories. Behavior-driven planning and navigation algorithms in mixed and unstructured traffic environments using game theory and risk-aware planning. Finally, I will conclude by discussing the future implications and broader applications of these ideas in the context of social robotics where robots are deployed in warehouses, restaurants, hospitals, and inside homes to assist human beings.