15 accepted papers
Network traffic analysis is crucial for maintaining the security of networks. Yet deploying accurate models on edge nodes remains challenging due to protocol diversity, complex traffic behaviors, and stringent resource constraints. Although recent deep learning models achieve strong performance, the
The combination of self-supervised learning and adversarial training (AT) can significantly improve the adversarial robustness of self-supervised models. However, the robustness of self-supervised adversarial training (self-AT) still lags behind that of state-of-the-art (SOTA) supervised AT (sup-AT)…
The emergence of Satellite-Terrestrial Integrated Networks (STIN) has significantly expanded terrestrial network coverage but introduced new security threats. Current Intrusion Detection Systems (IDSs) for STIN mostly consider the distributed nature of satellites, overlooking the computational limit…
Self-supervised learning (SSL) is a technique used to learn feature representations from unlabeled data. However, existing SSL frameworks either rely too heavily on domain knowledge due to their design based on feature invariance, leading to a lack of domain transferability, or they are based on aut…
Given a set of outlier-contaminated 2D–3D line correspondences between the scene and a captured image, we aim to recover the absolute camera pose. This is a fundamental problem in computer vision and robotics, for which many methods have been developed and shown impressive performance, but they fail
The ever-increasing complexity and scale of 5G communication networks pose huge challenges to network operations. Root cause analysis is considered as a promising method for fault detection. However, it still suffers challenges of severely uneven distribution of fault data, low accuracy in root caus…
Fisheye cameras, offering a wide field of view (FOV) of 360◦, are extensively employed for surround-view perception in autonomous driving. Compared with the object detection on the standard images, it lacks studies for fisheye images. Moreover, efficient perception is crucial for autonomous vehicles…
The increasing complexity and openness of 5G networks naturally enlarge the attack surface and introduce new vulnerabilities, thereby posing challenges to the performance of existing intrusion detection systems (IDSs). Current IDSs solely rely on statistical features, which may suffer from low accur…
This paper describes our submission to the ICASSP 2023 Signal Processing Grand Challenge (SPGC), which focuses on multilingual Alzheimer’s disease (AD) recognition through spontaneous speech. Our approaches include using a variety of acoustic features and silence-related information for AD detection…
This letter is concerned with the problem of estimating camera orientation from a set of 2D/3D line correspondences, which is a major part of the Perspective-n-Line (PnL) problem. There are some cases that usually occur in real applications for PnL: the input line correspondences are corrupted by mi
Relative pose estimation is a core task in robotic vision, and it is the basis of many high-level applications (e.g., visual odometry). In this letter, we focus on a quite common case in which the gravity direction is known in advance with the help of IMUs. Commonly, incorrect feature matches (a.k.a
Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR point clouds are typically large-scale and complexly distributed, which makes the registration challenging. In this paper, we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR p…
LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors,…
Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection, which are inefficient and not applicable in large scale scenes. T…
Three-dimensional rigid point cloud registration has many applications in computer vision and robotics. Local methods tend to fail, causing global methods to be needed, when the relative transformation is large or the overlap ratio is small. Most existing global methods utilize BnB optimization over…