While recent 3D-aware generative models have shown photo-realistic image...
Large-scale text-to-image models including Stable Diffusion are capable ...
Recent advances in diffusion models such as ControlNet have enabled
geom...
Interactive Recommender Systems (IRSs) have attracted a lot of attention...
In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework fo...
Test-time adaptation (TTA) aims to adapt a pre-trained model to the targ...
Existing text-to-image generation approaches have set high standards for...
Video-to-video translation aims to generate video frames of a target dom...
Test-time adaptation (TTA) methods, which generally rely on the model's
...
As recent advances in Neural Radiance Fields (NeRF) have enabled
high-fi...
In this paper, we propose PixelHuman, a novel human rendering model that...
Despite the extensive applications of relation extraction (RE) tasks in
...
3D content manipulation is an important computer vision task with many
r...
Lexically-constrained NMT (LNMT) aims to incorporate user-provided
termi...
In Reinforcement Learning (RL), enhancing sample efficiency is crucial,
...
Recently, unsupervised representation learning (URL) has improved the sa...
Despite the recent advances in open-domain dialogue systems, building a
...
Face swapping aims at injecting a source image's identity (i.e., facial
...
X-ray computed tomography (CT) is one of the most common imaging techniq...
Time-series forecasting models often encounter abrupt changes in a given...
This paper proposes a novel batch normalization strategy for test-time
a...
Single-image 3D human reconstruction aims to reconstruct the 3D textured...
In retrieval-based dialogue systems, a response selection model acts as ...
Time series forecasting has become a critical task due to its high
pract...
Existing deep interactive colorization models have focused on ways to ut...
Recently, deep learning-based methods have drawn huge attention due to t...
Incorporating personal preference is crucial in advanced machine transla...
Diagnosis based on medical images, such as X-ray images, often involves
...
Image classification models often learn to predict a class based on
irre...
Predicting traffic conditions is tremendously challenging since every ro...
Semantically meaningful sentence embeddings are important for numerous t...
Editing hairstyle is unique and challenging due to the complexity and
de...
With deep learning (DL) outperforming conventional methods for different...
In open-set recognition (OSR), classifiers should be able to reject
unkn...
Point-interactive image colorization aims to colorize grayscale images w...
Image-based virtual try-on aims to synthesize an image of a person weari...
Hairstyle transfer is the task of modifying a source hairstyle to a targ...
Image classifiers often rely overly on peripheral attributes that have a...
Despite recent advancements in deep learning, deep networks still suffer...
In image classification, "debiasing" aims to train a classifier to be le...
This paper presents a personalized character recommendation system for
M...
While NeRF-based 3D-aware image generation methods enable viewpoint cont...
In order to perform unconditional video generation, we must learn the
di...
While recent NeRF-based generative models achieve the generation of dive...
Image-based virtual try-on provides the capacity to transfer a clothing ...
Despite remarkable success in deep learning-based face-related models, t...
During the fine-tuning phase of transfer learning, the pretrained vocabu...
Despite the unprecedented improvement of face recognition, existing face...
Despite the impressive performance of deep networks in vision, language,...
Deep neural networks often make decisions based on the spurious correlat...