RI Seminar
Raj Reddy Assistant Professor in Robotics
Robotics Institute,
Carnegie Mellon University

Learning to Generalize beyond Training

Abstract: Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at cleaning floors, playing complex games, and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. One of the [...]

VASC Seminar
Sheng-Yu Wang
PhD Student
CMU

Detecting Image Synthesis — Shallow and Deep

Abstract: The proliferation of synthetic media are subject to malicious usages such as disinformation campaigns, posing potential threats to media integrity and democracy. A way to combat this is developing forensics algorithms to identify manipulated media. In the beginning of the talk, I will discuss how one can train a model to detect photos manipulated [...]

VASC Seminar
Sarah Aboutalib
Former Postdoctoral Scholar
University of Pittsburgh

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening

Abstract: Breast cancer screening using the standard mammography exam currently exhibits a high false recall rate (11.6% for women in the U.S.). Only a low proportion (0.5%) of women who were recalled for additional workup were actually found to have breast cancer. As a result of the unnecessary stress and follow-up work from these false [...]

VASC Seminar
Noah Snavely
Associate Professor
Cornell University and Google Research

The Plenoptic Camera

Abstract: Imagine a futuristic version of Google Street View that could dial up any possible place in the world, at any possible time. Effectively, such a service would be a recording of the plenoptic function—the hypothetical function described by Adelson and Bergen that captures all light rays passing through space at all times. While the plenoptic function [...]

VASC Seminar
Ricardo Martin-Brualla
Researcher
Google

Photorealistic Reconstruction of Landmarks and People using Implicit Scene Representation

Abstract: Reconstructing scenes to synthesize novel views is a long standing problem in Computer Vision and Graphics. Recently, implicit scene representations have shown novel view synthesis results of unprecedented quality, like the ones of Neural Radiance Fields (NeRF), which use the weights of a multi-layer perceptron to model the volumetric density and color of a [...]

VASC Seminar
Guoliang Kang
Postdoctoral Research Associate
LTI, CMU

Towards Discriminative and Domain-Invariant Feature Learning

Abstract: Deep neural networks have achieved great success in various visual applications, when trained with large amounts of labeled in-domain data. However, the networks usually suffer from a heavy performance drop on the data whose distribution is quite different from the training one. Domain adaptation methods aim to deal with such performance gap caused by [...]

VASC Seminar
Zhiqiang Shen
Postdoctoral Researcher
Department of Electrical & Computer Engineering, CMU

Learning Efficient Visual Representation on Model, Data, Label and Beyond

Abstract: Efficient deep learning is a broad concept that we aim to learn compressed deep models and develop training algorithms to improve the efficiency of model representations, data and label utilization, etc. In recent years, deep neural networks have been recognized as one of the most effective techniques for many learning tasks, also, in the [...]

VASC Seminar
Yannis Kalantidis
Research Scientist
NAVER LABS Europe

Self-supervised Learning and Generalization

Abstract: Contrastive self-supervised learning is a highly effective way of learning representations that are useful for, i.e. generalise, to a wide range of downstream vision tasks and datasets. In the first part of the talk, I will present MoCHi, our recently published contrastive self-supervised learning approach (NeurIPS 2020) that is able to learn transferable representations [...]

RI Seminar
Mac Schwager
Assistant Professor
Department of Aeronautics & Astronautics, Stanford University

Enabling Robots to Cooperate & Compete: Distributed Optimization & Game Theoretic Methods for Multiple Interacting Robots

Abstract: For robots to effectively operate in our world, they must master the skills of dynamic interaction.  Autonomous cars must safely negotiate their trajectories with other vehicles and pedestrians as they drive to their destinations.  UAVs must avoid collisions with other aircraft, as well as dynamic obstacles on the ground.  Disaster response robots must coordinate [...]

VASC Seminar
Bharath Hariharan
Assistant Professor
Cornell University

Learning to see from few labels

Abstract: Computer vision systems today exhibit a rich and accurate understanding of the visual world, but increasingly rely on learning on large labeled datasets to do so. This reliance on large labeled datasets is a problem especially when one considers difficult perception tasks, or novel domains where annotations might require effort or expertise. We thus [...]