47 accepted papers
Despite its resilience in adverse weather, millimeter-wave (mmWave) radar yields sparse and noisy point clouds that limit its perception and localization performance. Diffusion models have recently gained attention for enhancing millimeter-wave radar in perception tasks due to their strong denoising…
The advancement of data privacy regulations has spurred the development of Machine Unlearning (MU), which is designed to remove the influence of sensitive data from a trained model and results in an unlearned model (ULM). Despite rapid progress in MU techniques, their vulnerabilities remain underexp
Differentiable Gaussian Splatting (GS) has emerged as a powerful paradigm for scene representation, enabling efficient rendering and real-time editing. However, existing GS-based methods, which rely mainly on clear visual images, perform poorly in underwater environments due to camera distortions su…
Control Barrier Function (CBF) based quadratic programs (QPs) have become an effective method for enforcing safety in safety-critical systems and robotics. However, these methods often suffer from infeasibility or overly conservative relaxations when handling multiple constraints, potentially compro…
The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods p…
Search has emerged as core infrastructure for LLM-based agents and is widely viewed as critical on the path toward more general intelligence. Finance is a particularly demanding proving ground: analysts routinely conduct complex, multi-step searches over time-sensitive, domain-specific data, making…
Future prediction is a complex task for LLM agents, requiring a high level of analytical thinking, information gathering, contextual understanding, and decision-making under uncertainty. Agents must not only gather and interpret vast amounts of dynamic information but also integrate diverse data sou…
CLIP is a seminal multimodal model that maps images and text into a shared representation space by contrastive learning on billions of image–caption pairs. Inspired by the rapid progress of large language models (LLMs), we investigate how the superior linguistic understanding and broad world knowled
Forward-looking sonar is essential for underwater perception especially in turbid waters, yet its images are often strongly degraded by various noises, including speckle, sidelobe, and structural noises, which severely hinder downstream tasks such as underwater reconstruction, positioning, and navig…
Active visual tracking of underwater non-cooperative targets is a challenging task for autonomous underwater vehicles (AUVs) due to the complexity of underwater environments and the unpredictable dynamics of target motion. To address this challenge, this paper proposes UTracker, a novel framework fo
A comprehensive understanding of 3D scenes is essential for autonomous vehicles (AVs), and among various perception tasks, occupancy estimation plays a central role by providing a general representation of drivable and occupied space. However, most existing occupancy estimation methods rely on LiDAR
Differentiable Gaussian Splatting (GS) has emerged as a powerful paradigm for scene representation, enabling efficient rendering and real-time editing. However, existing GS-based methods, which rely mainly on clear visual images, perform poorly in underwater environments due to camera distortions su
Large deep learning models have achieved significant success in various tasks. However, the performance of a model can significantly degrade if it is needed to train on datasets with noisy labels with misleading or ambiguous information. To date, there are limited investigations on how to restore pe
Control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and th
This letter presents a novel weakly supervised semantic segmentation method for radar segmentation, where the existing LiDAR semantic segmentation models are employed to generate semantic labels, which then serve as supervision signals for training a radar semantic segmentation model. The obtained r
We present LiV-GS, a LiDAR-visual SLAM system in outdoor environments that leverages 3D Gaussian as a differentiable spatial representation. Notably, LiV-GS is the first method that directly aligns discrete and sparse LiDAR data with continuous differentiable Gaussian maps in large-scale outdoor sce
Multi-label causal feature selection has garnered considerable attention for its ability to identify the most informative features while accounting for the causal dependencies between labels and features. However, previous work often overlooks the unique contributions of labels to the target variabl
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects and…
Rib fracture patterns are key indicators of trauma severity. Detecting and locating these fractures is a critical yet time-consuming task, especially in 3D imaging, due to their minute size and irregular geometries. Existing voxel-based spatial methods fail to capture frequency-domain variations inh
We present Rad-GS, a 4D radar-camera SLAM system designed for kilometer-scale outdoor environments, utilizing 3D Gaussian as a differentiable spatial representation. Rad-GS combines the advantages of raw radar point cloud with Doppler information and geometrically enhanced point cloud to guide dynam
Machine Unlearning (MU) technology facilitates the removal of the influence of specific data instances from trained models on request. Despite rapid advancements in MU technology, its vulnerabilities are still underexplored, posing potential risks of privacy breaches through leaks of ostensibly unle
Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across individuals has prompted the development of deep learning models tailored to each subject. The personalization limits th…
Multimodal sensor fusion has been successfully utilized in many odometry and localization methods as it increases both estimate accuracy and robustness in application scenarios. To address the challenge of odometry under varying-weather conditions, we propose a novel visual 4D radar fusion based odo…
Accurate and robust geo-localization in all-weather conditions is essential for enabling autonomous vehicles and delivery robots to offer uninterrupted mobility services in the real world. In this paper, we propose the first camera and radar fusion based geo-localisation method that is robust to all…
Effective 3D reconstruction utilizing imaging sonars is vital for underwater robots, particularly in turbid water conditions. The absence of elevation angles in acoustic echo measurements significantly slows down the Neural Radiance Field (NeRF) method. This is attributed to the differentiable rende
Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potenti
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing tokens among spatial and sequential locations for unimodal representation or fusing clues of rumor veracity across modali…
Augmenting Language Models (LMs) with structured knowledge graphs (KGs) aims to leverage structured world knowledge to enhance the capability of LMs to complete knowledge-intensive tasks. However, existing methods are unable to effectively utilize the structured knowledge in a KG due to their inabil…
Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multimodal data, such as voiceprints and facial images. Recent distributed collaborative learning has been verified as an effe…
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms of *inefficient data utilization*. It relies on a single contrastive supervision for each image-text pair during repres…
Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. However, the research on fMRI-to-video reconstruction remains limited since decoding the spatiotemporal perception of conti…
Integrating data-driven and rule-based approaches is crucial for therapy recommendations since they can collaborate to achieve better performance. Medical rules, which are chains of reasoning that can infer therapies, widely exist. However, their symbolic and logical forms make integrating them with…
Due to the flexibility and ease of control, unmanned aerial vehicles (UAVs) have been increasingly used in various scenarios and applications in recent years. Training UAVs with reinforcement learning (RL) for a specific task is often expensive in terms of time and computation. However, it is known…
Abstractive related work generation has attracted increasing attention in generating coherent related work that helps readers grasp the current research. However, most existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the m…
Speaker recognition achieved great progress recently, however, it is not easy or efficient to further improve its performance via traditional solutions: collecting more data and designing new neural networks. Aiming at the fundamental challenge of speech data, i.e. low information density, multimoda…
Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics an…
Vision Transformer (ViT) faces obstacles in wide application due to its huge computational cost. Almost all existing studies on compressing ViT adopt the manner of splitting an image with a single granularity, with very few exploration of splitting an image with multi-granularity. As we know, import…
Clickbait posts tend to spread inaccurate or misleading information to manipulate people's attention and emotions, which greatly harms the credibility of social media. Existing clickbait detection models rely on analyzing the objective semantics in posts or correlating posts with article content onl…
Skeleton-based human action recognition and analysis have become increasingly attainable in many areas, such as security surveillance and anomaly detection. Given the prevalence of skeleton-based applications, tampering attacks on human skeletal features have emerged very recently. In particular, ch…
Building on deep representation learning, deep supervised hashing has achieved promising performance in tasks like similarity retrieval. However, conventional code balance constraints (i.e., bit balance and bit uncorrelation) imposed on avoiding overfitting and improving hash code quality are unsuit…
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). D…
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process and/…
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov’s method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are…
Most collaborative filtering (CF) models estimate missing ratings with an implicit assumption that the ratings are missing-at-random, which may cause the biased rating estimation and degraded performance since recent deep exploration shows that ratings may likely be missing-not-at-random (MNAR). To…
User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heteroge…