Traditional computer vision models often require extensive manual effort...
Model evolution and constant availability of data are two common phenome...
Generalized intent discovery aims to extend a closed-set in-domain inten...
Recently, many works have designed wider and deeper networks to achieve
...
Recovering clear structures from severely blurry inputs is a challenging...
Detecting out-of-domain (OOD) intents from user queries is essential for...
Discovering out-of-domain (OOD) intent is important for developing new s...
Recent advances in neural approaches greatly improve task-oriented dialo...
Detecting Out-of-Domain (OOD) or unknown intents from user queries is
es...
Out-of-Domain (OOD) detection is a key component in a task-oriented dial...
Traditional intent classification models are based on a pre-defined inte...
Accurate semantic segmentation models typically require significant
comp...
Click-Through Rate (CTR) prediction plays a key role in online advertisi...
Evolution of beliefs of a society are a product of interactions between
...
Recently, Graph Convolutional Network (GCN) has become a novel state-of-...
We consider the problem of omni-supervised object detection, which can u...
There are two main algorithmic approaches to autonomous driving systems:...
Despite plenty of efforts focusing on improving the domain adaptation ab...
Optimal transport (OT) formalizes the problem of finding an optimal coup...
A cognitive architecture aimed at cumulative learning must provide the
n...
Learning to capture feature relations effectively and efficiently is
ess...
Quantitative metrics that measure the global economy's equilibrium have
...
Tree skeleton plays an important role in tree structure analysis, forest...
The mixture cure rate model is the most commonly used cure rate model in...
Terrestrial laser scanning (TLS) can obtain tree point cloud with high
p...
We introduce an inversion based method, denoted as IMAge-Guided model
IN...
Extensive research in neural style transfer methods has shown that the
c...
Recent works of multi-source domain adaptation focus on learning a
domai...
Obtaining solutions to Optimal Transportation (OT) problems is typically...
We consider constrained policy optimization in Reinforcement Learning, w...
This paper introduces a new metamodel-based knowledge representation tha...
Blind image deblurring is a fundamental and challenging computer vision
...
Self-supervised depth estimation has shown its great effectiveness in
pr...
Terrestrial laser scanning technology provides an efficient and accuracy...
Autonomous navigation has played an increasingly significant role in
qua...
Long-tail recognition tackles the natural non-uniformly distributed data...
Intel SGX Guard eXtensions (SGX), a hardware-supported trusted execution...
The big data industry is facing new challenges as concerns about privacy...
The problem of counterfactual visual explanations is considered. A new f...
The accurate classification of plant organs is a key step in monitoring ...
Cooperation is often implicitly assumed when learning from other agents....
Point-cloud data acquired using a terrestrial laser scanner (TLS) play a...
We consider the problem of manifold learning. Extending existing approac...
Cooperative communication plays a central role in theories of human
cogn...
Cache-based side channels enable a dedicated attacker to reveal program
...
Complex network reconstruction is a hot topic in many fields. A popular
...
Cooperation information sharing is important to theories of human learni...
Discovering pulsars is a significant and meaningful research topic in th...
In this paper, a novel 3D deep learning network is proposed for brain MR...
In the multiple linear regression setting, we propose a general framewor...