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Found 13 papers, 6 papers with code
PointCutMix: Regularization Strategy for Point Cloud Classification
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent.
Ranked #3 on
3D Point Cloud Classification
on ModelNet40-C
3D Adversarial Attacks Beyond Point Cloud
In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced.
The art of defense: letting networks fool the attacker
To this end, in this paper, we propose a novel adversarial defense for 3D point cloud classifier that makes full use of the nature of the DNNs.
Adversarial samples for deep monocular 6D object pose estimation
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
Boosting 3D Adversarial Attacks with Attacking On Frequency
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks.
Robust Autonomous Landing of UAV in Non-Cooperative Environments based on Dynamic Time Camera-LiDAR Fusion
We have conducted extensive autonomous landing experiments in a variety of familiar or completely unknown environments, verifying that our model can adaptively balance the accuracy and speed, and the UAV can robustly select a safe landing site.
Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition
The adaptive adjusting term is composed of two complementary factors: 1) quantity factor, which pays more attention to tail classes, and 2) difficulty factor, which adaptively pays more attention to hard instances in the training process.
Knowledge Representation Learning with Contrastive Completion Coding
Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks.
Improving transferability of 3D adversarial attacks with scale and shear transformations
Extensive experiments show that the SS attack proposed in this paper can be seamlessly combined with the existing state-of-the-art (SOTA) 3D point cloud attack methods to form more powerful attack methods, and the SS attack improves the transferability over 3. 6 times compare to the baseline.
DASTSiam: Spatio-Temporal Fusion and Discriminative Augmentation for Improved Siamese Tracking
Tracking tasks based on deep neural networks have greatly improved with the emergence of Siamese trackers.
Robotic Fabric Flattening with Wrinkle Direction Detection
Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc.
Learning to bag with a simulation-free reinforcement learning framework for robots
Bagging is an essential skill that humans perform in their daily activities.
DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects
We present DexDLO, a model-free framework that learns dexterous dynamic manipulation policies for deformable linear objects with a fixed-base dexterous hand in an end-to-end way.
