Large models have emerged as the most recent groundbreaking achievements...
Graph data augmentation has proven to be effective in enhancing the
gene...
Given a small set of images of a specific subject, subject-driven
text-t...
Click-Through Rate (CTR) prediction is the most critical task in product...
Graph Neural Networks (GNNs) obtain tremendous success in modeling relat...
Graph machine learning has been extensively studied in both academia and...
Disentangled Representation Learning (DRL) aims to learn a model capable...
Domain generalization (DG) aims at generalizing a classifier trained on
...
We present the design and baseline results for a new challenge in the
Ch...
Graph neural networks (GNNs) have achieved tremendous success in the tas...
Graph neural architecture search (GraphNAS) has recently aroused conside...
Clustering is a fundamental machine learning task which has been widely
...
Although deep neural networks are capable of achieving performance super...
Conventional model quantization methods use a fixed quantization scheme ...
Various neural network models have been proposed to tackle combinatorial...
The exponentially large discrete search space in mixed-precision quantiz...
Temporal Sentence Grounding in Videos (TSGV), which aims to ground a nat...
In this work, we present a fully self-supervised framework for semantic
...
Graph machine learning has been extensively studied in both academia and...
Conventional machine learning (ML) relies heavily on manual design from
...
Graph machine learning has been extensively studied in both academic and...
Geometric deep learning, i.e., designing neural networks to handle the
u...
Graph neural networks (GNNs) have achieved impressive performance when
t...
In this paper, we investigate a novel and challenging task, namely
contr...
Enhancing the diversity of sentences to describe video contents is an
im...
Knowledge graph is generally incorporated into recommender systems to im...
Temporal sentence grounding in videos(TSGV), which aims to localize one
...
Models trained with offline data often suffer from continual distributio...
With the success of the graph embedding model in both academic and indus...
In this paper, we investigate the recent studies on multimedia edge
comp...
Recent years have witnessed an upsurge of research interests and applica...
Machine learning on graphs has been extensively studied in both academic...
Meta-learning aims at learning quickly on novel tasks with limited data ...
Despite Temporal Sentence Grounding in Videos (TSGV) has realized impres...
With the outbreak of COVID-19, how to mitigate and suppress its spread i...
Curriculum learning (CL) is a training strategy that trains a machine
le...
Graph Neural Networks (GNNs) are widely used deep learning models that l...
Graph neural networks (GNNs) are emerging machine learning models on gra...
Graph Neural Networks (GNNs) are emerging machine learning models on gra...
Variants of Graph Neural Networks (GNNs) for representation learning hav...
Mining graph data has become a popular research topic in computer scienc...
Temporal sentence grounding in videos aims to detect and localize one ta...
User behavior data in recommender systems are driven by the complex
inte...
Designing accurate and efficient convolutional neural architectures for ...
With the rapid development of Internet and multimedia services in the pa...
With the growth of computer vision based applications and services, an
e...
With the tremendous growth of videos over the Internet, video thumbnails...
With the great success of graph embedding model on both academic and ind...
With the great success of Graph Neural Networks (GNNs) towards represent...
Graph convolutional networks (GCNs) are powerful tools for graph-structu...