Vision Transformers achieve impressive accuracy across a range of visual...
Deep neural networks have been widely used in various downstream tasks,
...
This report describes our submission to the Ego4D Moment Queries Challen...
Deep models have demonstrated recent success in single-image dehazing. M...
The abundance of instructional videos and their narrations over the Inte...
Recent state-of-the-art methods in imbalanced semi-supervised learning (...
This report describes our submission to the Ego4D Moment Queries Challen...
This report describes Badgers@UW-Madison, our submission to the Ego4D Na...
Time-resolved image sensors that capture light at pico-to-nanosecond
tim...
The ability to estimate 3D human body pose and movement, also known as h...
We demonstrate the utility of deep learning for modeling the clustering ...
Scene inference under low-light is a challenging problem due to severe n...
Forward modeling approaches in cosmology have made it possible to recons...
Recent state-of-the-art methods in semi-supervised learning (SSL) combin...
We build a field level emulator for cosmic structure formation that is
a...
We train a neural network model to predict the full phase space evolutio...
Computational approach to imaging around the corner, or non-line-of-sigh...
Self-attention based Transformer models have demonstrated impressive res...
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS...
Efficient and adaptive computer vision systems have been proposed to mak...
Contrastive language-image pretraining (CLIP) using image-text pairs has...
Visual content creation has spurred a soaring interest given its applica...
Generative deep learning methods built upon Convolutional Neural Network...
We present the Cosmology and Astrophysics with MachinE Learning Simulati...
We train neural networks to perform likelihood-free inference from
(25 h...
Astrophysical processes such as feedback from supernovae and active gala...
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce
...
Learning from image-text data has demonstrated recent success for many
r...
Modern deep learning models require large amounts of accurately annotate...
The mining and utilization of features directly affect the classificatio...
Among the most extreme objects in the Universe, active galactic nuclei (...
Given a video captured from a first person perspective and recorded in a...
This paper proposes Prism, a secret sharing based approach to compute pr...
Transformers have emerged as a powerful tool for a broad range of natura...
Whole slide images (WSIs) have large resolutions and usually lack locali...
Advanced video analytic systems, including scene classification and obje...
We address the challenging problem of image captioning by revisiting the...
Weakly supervised phrase grounding aims at learning region-phrase
corres...
We address the task of jointly determining what a person is doing and wh...
We present an interpretable deep model for fine-grained visual recogniti...
Despite exciting progress on cryptography, secure and efficient query
pr...
We address the challenging problem of deep representation learning–the
e...
We address the challenging task of anticipating human-object interaction...
We introduce a novel deep neural network architecture that links visual
...
We address the challenging problem of learning motion representations us...
Matter evolved under influence of gravity from minuscule density
fluctua...
Can a robot grasp an unknown object without seeing it? In this paper, we...
Data outsourcing allows data owners to keep their data at untrusted clou...
Edge detection has made significant progress with the help of deep
Convo...
This paper investigates two-branch neural networks for image-text matchi...