15 accepted papers
Multi-view clustering of remote sensing data plays a vital role in Earth observation analysis. Recently, deep graph clustering methods based on contrastive learning have significantly improved feature representation capabilities. However, most existing approaches treat all views equally, neglecting
Recent advancements in end-to-end autonomous driving systems (ADSs) underscore their potential for perception and planning capabilities. However, challenges remain. Complex driving scenarios contain rich semantic information, yet ambiguous or noisy semantics can compromise decision reliability, whil
Federated graph learning (FGL), which excels in analyzing non-IID graphs as well as protecting data privacy, has recently emerged as a hot topic. Existing FGL methods usually train the client model using labeled data and then collaboratively learn a global model without sharing their local graph dat…
Deep clustering with attribute-missing graphs, where only a subset of nodes possesses complete attributes while those of others are missing, is an important yet challenging topic in various practical applications. It has become a prevalent learning paradigm in existing studies to perform data imputa…
We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages
Deep reinforcement learning struggles to generalize across tasks that remain unseen during training. Consider a neural process observed in humans and animals, where they not only learn new solutions but also deduce shared subroutines. These subroutines can be applied to tasks involving similar state…
Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency. They are important in Low-Resource Language (LRL) Neural Machine Translation (NMT) to enh…
Automated synthesis of zeolite, one of the most important catalysts in chemical industries, holds great significance for attaining economic and environmental benefits. Structural synthesis data extracted through NLP technologies from zeolite experimental procedures can significantly expedite automat…
Program synthesis aims to automatically generate an executable program that conforms to the given specification. Recent advancements have demonstrated that deep neural methodologies and large-scale pretrained language models are highly proficient in capturing program semantics. For robot programming…
Event-based visual perception is becoming increasingly popular owing to interesting sensor characteristics enabling the handling of difficult conditions such as highly dynamic motion or challenging illumination. The mostly complementary nature of event cameras however still means that best results a…
The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the m…
Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. However, different from traditional frame-based cameras, event cameras measure asynchronous pixel-level brightness changes and return them in a highly discretised format, hence new algorithm…
The increasing availability of surround-view camera systems in passenger vehicles motivates their use as an exterior perception modality for intelligent vehicle behaviour. An important problem within this context is the extrinsic calibration between the cameras, which is challenging due to the often…
Modern vehicles are often equipped with a surround-view multi-camera system. The current interest in autonomous driving invites the investigation of how to use such systems for a reliable estimation of relative vehicle displacement. Existing camera pose algorithms either work for a single camera, ma…
While monocular and stereo camera based motion estimation has reached a level of maturity that enables industrial use, the community keeps exploring novel multi-sensor solutions to meet the high robustness and accuracy requirements of certain applications such as autonomous vehicles. The present pap…