DETR-based object detectors have achieved remarkable performance but are...
Vision-Language (V-L) models trained with contrastive learning to align ...
This paper is on Few-Shot Object Detection (FSOD), where given a few
tem...
Prompt tuning provides an efficient mechanism to adapt large vision-lang...
This paper is on soft prompt learning for Vision & Language (V L) mode...
This work is on training a generative action/video recognition model who...
This paper tackles the problem of efficient video recognition. In this a...
Learning visual representations through self-supervision is an extremely...
Existing knowledge distillation methods mostly focus on distillation of
...
Self-attention based models such as vision transformers (ViTs) have emer...
Deep Learning models based on heatmap regression have revolutionized the...
We propose defensive tensorization, an adversarial defence technique tha...
This report presents the technical details of our submission to the
EPIC...
This paper is on video recognition using Transformers. Very recent attem...
Mixed-precision networks allow for a variable bit-width quantization for...
What is the best way to learn a universal face representation? Recent wo...
An important component of unsupervised learning by instance-based
discri...
This paper tackles the challenging problem of estimating the intensity o...
Network binarization is a promising hardware-aware direction for creatin...
Action Units (AUs) are geometrically-based atomic facial muscle movement...
This paper shows how to train binary networks to within a few percent po...
This paper addresses the problem of model compression via knowledge
dist...
This paper proposes Binary ArchitecTure Search (BATS), a framework that
...
This paper is on highly accurate and highly efficient human pose estimat...
Lip-reading models have been significantly improved recently thanks to
p...
This paper proposes an improved training algorithm for binary neural net...
With the unprecedented success of deep convolutional neural networks cam...
This paper is on improving the training of binary neural networks in whi...
The prominence of deep learning, large amount of annotated data and
incr...
Big neural networks trained on large datasets have advanced the
state-of...
Recent findings indicate that over-parametrization, while crucial for
su...
Our goal is to design architectures that retain the groundbreaking
perfo...
This paper is on image and face super-resolution. The vast majority of p...
This paper addresses two challenging tasks: improving the quality of
rea...
3D face reconstruction is a fundamental Computer Vision problem of
extra...
This paper investigates how far a very deep neural network is from attai...
Our goal is to design architectures that retain the groundbreaking
perfo...
This paper describes our submission to the 1st 3D Face Alignment in the ...
This paper is on human pose estimation using Convolutional Neural Networ...