33 accepted papers
Most adversarial attacks on point clouds perturb a large number of points, causing widespread geometric changes and limiting applicability in real-world scenarios. While recent works explore sparse attacks by modifying only a few points, such approaches often struggle to maintain effectiveness due t
The vulnerability of deep neural networks to adversarial examples poses significant challenges to their reliable deployment. Among existing empirical defenses, adversarial training and robust distillation have proven the most effective. In this paper, we identify a property originally associated wit…
Deep neural networks (DNNs) often produce overconfident predictions on out-of-distribution (OOD) inputs, undermining their reliability in open-world environments. Singularities in semi-discrete optimal transport (OT) mark regions of semantic ambiguity, where classifiers are particularly prone to unw…
Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these VLMs are task-specific and assume that both video and language inputs are complete. However, real-world VLM applications might face challenges due
Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality a
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable reasoning capabilities across various downstream multimodal tasks, they are proven to be vulnerable to carefully designed adversarial examples. Existing LVLM attackers show that exploring external components of adversarial gu
Adversarial attacks on robotic grasping provide valuable insights into evaluating and improving the robustness of these systems. Unlike studies that focus solely on neural network predictions while overlooking the physical principles of grasping, this paper introduces AdvGrasp, a framework for adver
Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the comp
Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited at…
Imperceptible adversarial attacks on 3D point clouds rely on effective constraints. While manifold constraints have notable advantages over Euclidean ones, the global parameterization used in current methods often fails to fully preserve manifold properties. In this paper, we propose to constrain la…
Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Traditional solutions strictly limit point displacement during attacks, making it challenging to balance imperceptibility with adversarial effectiveness. In this paper,…
Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought (CoT) reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning. While parameter-efficient fine-tuning (PEFT) helps reduce cost, mos
Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train each video-query pair separately and ignore the re…
Deep neural networks (DNNs) often exhibit out-of-distribution (OOD) overconfidence, producing overly confident predictions on OOD samples. We attribute this issue to the inherent over-complexity of DNNs and investigate two key aspects: capacity and nonlinearity. First, we demonstrate that reducing m…
Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in S
Although Large Vision-Language Models (LVLMs) exhibit impressive multimodal capabilities, their vulnerability to adversarial examples has raised serious security concerns. Existing LVLM attackers simply optimize adversarial images that easily overfit a certain model/prompt, making them ineffective o…
Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial att…
Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) sample…
Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterances in unannotated conversations, this task that has garnered increasing attention recently. Previous methods often apply Emotion-Cause Pair Extraction (ECPE) task models,…
"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 unsatisfactory imperceptibility due to inadequate considera…
Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we address a more challenging task, rumor source det…
"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 attackers rely on implicit global distance losses to pertu…
Language models have shown vulnerability against backdoor attacks, threatening the security of services based on them. To mitigate the threat, existing solutions attempted to search for backdoor triggers, which can be time-consuming when handling a large search space. Looking into the attack process…
Adversarial attacks on 3D point clouds often exhibit unsatisfactory imperceptibility, which primarily stems from the disregard for manifold-aware distortion, i.e., distortion of the underlying 2-manifold surfaces. In this paper, we develop novel manifold constraints to reduce such distortion, aiming…
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding tasks. Nevertheless, these models are susceptible to adversarial examples. In real-world applications, existing LVLM attackers generally rely on the detailed prior knowledge…
Reparameterization techniques have demonstrated their efficacy in improving the efficiency of deep neural networks. However, their application has been largely confined to single-input network structures, leaving multi-input ones, commonly encountered in real-world applications, largely unexplored.…
"This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that utilizes contrastive learning and reconstruction paradigm for scoring the pre-defined moment proposals. Although they have…
This paper addresses the task of temporal activity localization (TAL). Although recent works have made significant progress in TAL research, almost all of them implicitly assume that the dense frame-level correspondences in each video-query pair are correctly annotated. However, in reality, such an…
This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topic, they severely rely on massive expensive video-query paired annotations, which require a tremendous amount of human effort to collect in real-worl…
Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the un…
Manipulation plays a vital role in robotics but is left unsolved. Recent work attempts to leverage the hierarchical structure of tasks via using action primitives. However, due to trajectory distribution shift, prior action primitives could hardly be adapted to new tasks. In this letter, we propose
Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks. The key to resolve the OOD overconfidence issue inherently is to build a subset of OOD samples and then suppress predictions on them. This paper proposes the Chamfer OOD examples (CODEs), whose…
Soft-robotic manipulators, with their closed-chamber elastomeric actuators, natural water-sealing and inherent compliance, are ideal for underwater applications for compact, lightweight, and dexterous manipulation tasks. However, their low structure rigidity makes soft robots highly prone to underwa