In this report, we introduce NICE
project[<https://nice.lgresearch.ai/>]...
Transfer learning is widely used for training deep neural networks (DNN)...
This work focuses on the problem of reconstructing a 3D human body mesh ...
Recent advances in deep learning have significantly improved the perform...
Mixed sample data augmentation (MSDA) is a widely used technique that ha...
Steganography is the process of embedding secret data into another messa...
From an image of a person, we can easily infer the natural 3D pose and s...
Depth estimation is an important task in various robotics systems and
ap...
Portrait stylization, which translates a real human face image into an
a...
Since the beginning of world-wide COVID-19 pandemic, facial masks have b...
Model inversion attacks are a type of privacy attack that reconstructs
p...
Light field (LF) camera captures rich information from a scene. Using th...
As the size of the dataset used in deep learning tasks increases, the no...
Uncertainty estimation of the trained deep learning network provides
imp...
Generally, regularization-based continual learning models limit access t...
We present a novel adversarial penalized self-knowledge distillation met...
Pre-training vision-language models with contrastive objectives has show...
Pre-trained representation is one of the key elements in the success of
...
Non-local (NL) block is a popular module that demonstrates the capabilit...
When a high-resolution (HR) image is degraded into a low-resolution (LR)...
Despite its importance, unsupervised domain adaptation (UDA) on LiDAR
se...
Transfer learning of StyleGAN has recently shown great potential to solv...
Recent self-supervised video representation learning methods focus on
ma...
Understanding point cloud has recently gained huge interests following t...
Depth estimation from a single image is an important task that can be ap...
With the development of 3D scanning technologies, 3D vision tasks have b...
Existing 3D human pose estimation algorithms trained on distortion-free
...
Recent weakly-supervised semantic segmentation (WSSS) has made remarkabl...
Batch Whitening is a technique that accelerates and stabilizes training ...
We present a new approach for oriented object detection, an anchor-free
...
Training of Convolutional Neural Networks (CNNs) with data with noisy la...
Self-supervised contrastive learning offers a means of learning informat...
In active learning, the focus is mainly on the selection strategy of
unl...
Following considerable development in 3D scanning technologies, many stu...
Scene understanding is an essential technique in semantic segmentation.
...
Although well-trained deep neural networks have shown remarkable perform...
Advances in technology have led to the development of methods that can c...
We propose a quadratic penalty method for continual learning of neural
n...
We propose a novel continual learning method called Residual Continual
L...
In this paper, we propose a Generative Translation Classification Networ...
Training of deep neural networks heavily depends on the data distributio...
Transferrable neural architecture search can be viewed as a binary
optim...
Object instance detection in cluttered indoor environment is a core
func...
Convolutional Neural Networks (CNNs) provide excellent performance when ...
We introduce Repetition-Reduction network (RRNet) for resource-constrain...
We propose a novel regularization algorithm to train deep neural network...
Convolutional networks have achieved great success in various vision tas...
Generating a novel image by manipulating two input images is an interest...
Expanding the domain that deep neural network has already learned withou...
Deep neural networks (DNNs) have shown the state-of-the-art level of
per...