Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Despite leveraging various geometric constraints, current adversarial attack strategies often suffer from inadequate ...

With the development of depth sensors and 3D vision, the vulnerability of 3D point cloud models has garnered heightened concern. Almost all existing 3D attackers are deployed in the white-box setting, where they access the model details and directly ...

Transformers have demonstrated potential in leveraging features for two-stage human-object interaction (HOI) detection, but a considerable performance gap persists compared to one-stage methods. We attribute this discrepancy to the limited ...

With the maturity of depth sensors, point clouds have received increasing attention in various 3D safety-critical applications, while deep point cloud learning models have been shown to be vulnerable to adversarial attacks. Most existing 3D ...

Adversarial attacks on point clouds play a vital role in assessing and enhancing the adversarial robustness of 3D deep learning models. While employing a variety of geometric constraints, existing adversarial attack solutions often display ...

Many works have shown that the adversarial examples being generated on a known substitute model have the ability to mislead other unknown black-box models, which has attracted widespread attention. Recently, many model augmentation methods have ...

Defect recognition is an important task in intelligent manufacturing. Due to the subjectivity of human annotation, the collected defect data usually contains a lot of noise and unpredictable uncertainties, which have a great negative influence on defect ...

Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention ...

Graph Convolutional Networks (GCNs) are widely used for skeleton-based action recognition and achieved remarkable performance. Due to the locality of graph convolution, GCNs can only utilize short-range node dependencies but fail to model long-range node ...

Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and enhancing their adversarial robustness. Among various attack types, hard-label attacks that rely only on predicted labels offer a practical approach. This paper ...

In practice, deep learning models are easy to be fooled by input images with subtle perturbations, and those images are called adversarial examples. Regarding one model, the crafted adversarial examples can successfully fool other models with ...

Adversarial training (AT) is one of the most promising solutions for defending adversarial attacks. By exploiting the adversarial examples generated in the maximization step of AT, a large improvement on the robustness can be brought. However, by ...${}^{\mathrm{}}$${}^{\mathrm{}}$

Graph-based unsupervised feature selection algorithms have been shown to be promising for handling unlabeled and high-dimensional data. Whereas, the vast majority of those algorithms usually involve two independent processes, i.e., similarity ...

During the information exploding era, news can be created or edited purposely for promoting the spreading of social influence. However, unverified or fabricated news can also spread unscrupulously, leading to serious consequences, such as poor ...

• We present a new multimodal fake news detection framework with contrastive learning.
• The combination of back-translation and contrastive learning is proved effective.
• The specific effects of contrastive learning and different image ...

Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Existing attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate ...

As science advances and machines become larger and more sophisticated, it is vital to determine whether there is a mechanical failure without damaging the device. Stochastic resonance (SR), as a widely used method, can effectively extract the periodic ...

Designing efficient deep learning models for 3D point clouds is an important research topic. Point-voxel convolution (Liu et al. in NeurIPS, 2019) is a pioneering approach in this direction, but it still has considerable room for improvement in ...

In the real world, blackbox attacks seem to be widely existed due to the lack of detailed information of models to be attacked. Hence, it is desirable to obtain adversarial examples with high transferability which will facilitate practical ...

3D face reconstruction has witnessed considerable progress in recovering 3D face shapes and textures from in-the-wild images. However, due to a lack of texture detail information, the reconstructed shape and texture based on deep learning could not be ...

Although data-driven models, especially deep learning, have achieved astonishing results on many prediction tasks for nonlinear sequences, challenges still remain in finding an appropriate way to embed prior knowledge of physical dynamics in these models. ...