9 accepted papers
Vertical Federated Learning (VFL) is a distributed machine learning paradigm in which participants train models with vertically partitioned data. Many previous studies have identified backdoor vulnerabilities in VFL systems. However, limited effort has been devoted to developing defenses against suc
Data heterogeneity and backdoor attacks rank among the most significant challenges facing federated learning (FL). For data heterogeneity, personalized federated learning (PFL) enables each client to maintain a private personalized model to cater to client-specific knowledge. Meanwhile, vanilla FL h…
3D point cloud semantic segmentation is a challenging topic in the computer vision field. Most of the existing methods in literature require a large amount of fully labeled training data, but it is extremely time-consuming to obtain these training data by manually labeling massive point clouds. Addr…
Odometer-aided visual-inertial SLAM systems typically have a good performance for navigation of wheeled platforms, while they usually suffer from degenerate cases before the first turning. In this paper, firstly we perform an observability analysis w.r.t. the extrinsic parameters before the first tu
Transductive zero-shot learning (T-ZSL) which could alleviate the domain shift problem in existing ZSL works, has received much attention recently. However, an open problem in T-ZSL: how to effectively make use of unseen-class samples for training, still remains. Addressing this problem, we first em…
Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically,…
Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration…
In this paper we present an effcient modeling framework for large scale urban scenes. Taking surface meshes derived from multi- view-stereo systems as input, our algorithm outputs simplied models with semantics at different levels of detail (LODs). Our key observation is that urban building is usual…
Structure-from-Motion (SfM) methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. While incremental system has advanced in robustness and accuracy, the efficiency remains its key challenge. To solve this problem, global reconstruction s…