VASC Seminar
Vineeth N Balasubramanian
Associate Professor
Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad

Going Beyond Continual Learning: Towards Organic Lifelong Learning

3305 Newell-Simon Hall

Abstract: Supervised learning, the harbinger of machine learning over the last decade, has had tremendous impact across application domains in recent years. However, the notion of a static trained machine learning model is becoming increasingly limiting, as these models are deployed in changing and evolving environments. Among a few related settings, continual learning has gained significant [...]

VASC Seminar
Santhosh Kumar Ramakrishnan
Ph.D. Candidate
University of Texas at Austin

Predictive Scene Representations for Embodied Visual Search

GHC 6501

Abstract:  My research advances embodied AI by developing large-scale datasets and state-of-the-art algorithms. In my talk, I will specifically focus on the embodied visual search problem, which aims to enable intelligent search for robots and augmented reality (AR) assistants. Embodied visual search manifests as the visual navigation problem in robotics, where a mobile agent must efficiently navigate [...]

VASC Seminar
Aayush Bansal
Startup

Generating Beautiful Pixels

Newell-Simon Hall 3305

Abstract: In this talk, I will present three experiments that use low-level image statistics to generate high-resolution detailed outputs. In the first experiment, I will use 2D pixels to efficiently mine hard examples for better learning. Simply biasing ray sampling towards hard ray examples enables learning of neural fields with more accurate high-frequency detail in less [...]

VASC Seminar
Viraj Prabhu
CS PhD Student
Georgia Institute of Technology

Towards Reliable Computer Vision Systems

Newell-Simon Hall 3305

Abstract:  The real world has infinite visual variation – across viewpoints, time, space, and curation. As deep visual models become ubiquitous in high-stakes applications, their ability to generalize across such variation becomes increasingly important. In this talk, I will present opportunities to improve such generalization at different stages of the ML lifecycle: first, I will [...]

VASC Seminar
Bharath Hariharan
Assistant Professor
Cornell University

Vision without labels

3305 Newell-Simon Hall

Abstract: Deep learning has revolutionized all aspects of computer vision, but its successes have come from supervised learning at scale: large models trained on ever larger labeled datasets. However this reliance on labels makes these systems fragile when it comes to new scenarios or new tasks where labels are unavailable. This is in stark contrast to [...]

VASC Seminar
Yong Jae Lee
Associate Professor
Department of Computer Sciences , University of Wisconsin-Madison

Large Multimodal (Vision-Language) Models for Image Generation and Understanding

Newell-Simon Hall 3305

Abstract: Large Language Models and Large Vision Models, also known as Foundation Models, have led to unprecedented advances in language understanding, visual understanding, and AI. In particular, many computer vision problems including image classification, object detection, and image generation have benefited from the capabilities of such models trained on internet-scale text and visual data. In [...]

VASC Seminar
Mohamed Elhoseiny
Assistant Professor
Computer Science, KAUST

Imaginative Vision Language Models: Towards human-level imaginative AI skills transforming species discovery, content creation, self-driving cars, and emotional health

3305 Newell-Simon Hall

Abstract:   Most existing AI learning methods can be categorized into supervised, semi-supervised, and unsupervised methods. These approaches rely on defining empirical risks or losses on the provided labeled and/or unlabeled data. Beyond extracting learning signals from labeled/unlabeled training data, we will reflect in this talk on a class of methods that can learn beyond the vocabulary [...]

VASC Seminar
Kenneth Marino
Research Scientist
Google DeepMind

World Knowledge in the Time of Large Models

Newell-Simon Hall 3305

Abstract:  This talk will discuss the massive shift that has come about in the vision and ML community as a result of the large pre-trained language and language and vision models such as Flamingo, GPT-4, and other models. We begin by looking at the work on knowledge-based systems in CV and robotics before the large model [...]

VASC Seminar
Shunsuke Saito
Research Scientist
Meta Reality Labs Research

Digital Human Modeling with Light

Newell-Simon Hall 3305

Abstract: Leveraging light in various ways, we can observe and model physical phenomena or states which may not be possible to observe otherwise. In this talk, I will introduce our recent exploration on digital human modeling with different types of light. First, I will present our recent work on the modeling of relightable human heads, [...]

VASC Seminar
Jonathon Luiten
Postdoctoral Fellow
RWTH Aachen and Carnegie Mellon University

Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis

Newell-Simon Hall 3305

Abstract: We present a method that simultaneously addresses the tasks of dynamic scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We follow an analysis-by-synthesis framework, inspired by recent work that models scenes as a collection of 3D Gaussians which are optimized to reconstruct input images via differentiable rendering. To model [...]