Generating motion for robots that interact with objects of various shape...
Contrastive Language-Audio Pretraining (CLAP) is pre-trained to associat...
Learning meaningful frame-wise features on a partially labeled dataset i...
Deep neural networks are valuable assets considering their commercial
be...
We present a novel defense, against backdoor attacks on Deep Neural Netw...
Deep neural networks (DNNs) are widely deployed on real-world devices.
C...
Deep neural networks (DNNs) have been widely and successfully adopted an...
In this work, we propose to learn robot geometry as distance fields (RDF...
In this study, we investigated the potential of ChatGPT, a large languag...
Object detection and multiple object tracking (MOT) are essential compon...
Multiple robots could perceive a scene (e.g., detect objects) collaborat...
We propose DeepExplorer, a simple and lightweight metric-free exploratio...
Training deep neural networks (DNNs) usually requires massive training d...
Humans can easily imagine the complete 3D geometry of occluded objects a...
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where
ad...
Third-party resources (e.g., samples, backbones, and pre-trained models)...
LiDAR mapping is important yet challenging in self-driving and mobile
ro...
Visual object tracking is an essential capability of intelligent robots....
Grasping with anthropomorphic robotic hands involves much more hand-obje...
Recent studies revealed that deep neural networks (DNNs) are exposed to
...
Deep neural networks (DNNs) are vulnerable to backdoor attacks. The back...
Recent studies have demonstrated that deep neural networks (DNNs) are
vu...
In this paper, we describe in detail our system for DCASE 2022 Task4. Th...
Deep neural networks (DNNs) have demonstrated their superiority in pract...
Sharing information between connected and autonomous vehicles (CAVs)
fun...
Visual place recognition (VPR) using deep networks has achieved
state-of...
Image representation is critical for many visual tasks. Instead of
repre...
Currently, deep neural networks (DNNs) are widely adopted in different
a...
We are interested in anticipating as early as possible the target locati...
Knowledge of molecular subtypes of gliomas can provide valuable informat...
Vehicle-to-everything (V2X), which denotes the collaboration between a
v...
Recent studies have revealed that deep neural networks (DNNs) are vulner...
Visual object tracking (VOT) has been widely adopted in mission-critical...
Obtaining a well-trained model involves expensive data collection and
tr...
Hypertension is the leading global cause of cardiovascular disease and
p...
To promote better performance-bandwidth trade-off for multi-agent percep...
Quantitative estimation of the acute ischemic infarct is crucial to impr...
Adversarial training (AT) has been demonstrated as one of the most promi...
Grasping in cluttered scenes has always been a great challenge for robot...
Most existing Siamese-based tracking methods execute the classification ...
Recently, the Siamese-based method has stood out from multitudinous trac...
Backdoor attack intends to inject hidden backdoor into the deep neural
n...
LiDAR point clouds collected from a moving vehicle are functions of its
...
As a crucial robotic perception capability, visual tracking has been
int...
Deep neural networks (DNNs) are vulnerable to the backdoor attack,
which...
As the successor of H.265/HEVC, the new versatile video coding standard
...
To explore the vulnerability of deep neural networks (DNNs), many attack...
In the domain of visual tracking, most deep learning-based trackers high...
Recently, backdoor attacks pose a new security threat to the training pr...
We consider the problem of representation learning for temporal interact...