45 accepted papers
Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reli
Generalized vehicle trajectory prediction across diverse junctions, including urban intersections and roundabouts, remains a fundamental task in Cooperative Vehicle–Infrastructure Systems (CVIS). This study faces two key challenges: (1) Generalize across junctions with heterogeneous map semantics an
Recent advances in learning-based Light Field (LF) image denoising have achieved impressive results. However, these methods rely heavily on large-scale noisy-clean image pairs and often fail to generalize to unseen or complex noise.In this work, we observe that the inherent multi-view consistency of
Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging. Existing methods typically treat the agent as an indivisible entity, modeling motion patterns globally. Such global modeling is tightly coupled with the morphology, hindering transfe
Achieving cross-task generalization remains a critical challenge in Multi-Agent Reinforcement Learning (MARL), fundamentally relying on effective inductive biases. However, existing entity-level biases often overlook collaborative patterns, whereas task-level biases lack sufficient coverage for nove…
Offline reinforcement learning (RL) has achieved notable progress in recent years. However, most existing offline RL methods require a large amount of training data to achieve reasonable performance and offer limited out-of-distribution (OOD) generalization capability due to conservative data-relate…
In multi-agent systems, explicit cognition of teammates
Spatio-temporal graph networks form the foundation of modern traffic prediction, yet their deployment is fundamentally challenged by the pervasive reality of distribution shifts. While out-of-distribution (OOD) learning holds promise for robustness, existing methods rely on static graph structures,
Accurate trajectory prediction of multiple road users at urban intersections--including motorized and nonmotorized vehicles and pedestrians--is critical for cooperative vehicle-infrastructure systems and intelligent transportation systems. This study focuses on multiple road users' trajectory predic
Recent Continual Learning (CL)-based Temporal Knowledge Graph Reasoning (TKGR) methods focus on significantly reducing computational cost and mitigating catastrophic forgetting caused by fine-tuning models with new data. However, existing CL-based TKGR methods still face two key limitations: (1) The…
Advanced deep spatio-temporal networks have become the mainstream for traffic prediction, but the widespread adoption of these models is impeded by the prevalent scarcity of available data. Despite cross-city transfer learning emerging as a common strategy to address this issue, it overlooks the inh
Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challenging to meet in practice. However, real-world agents suffer from channel delays, receiving messages sent at different time…
Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these…
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T). Accurately quantifying this uncertainty requires generating the most likely path and assessing travel time uncertainty a…
Disparity estimation is an essential step in processing and analyzing Light Field (LF) images. Recent methods construct the cost volume to exploit the correspondence of the LFs over the preset maximum disparity, limiting them to process the large parallax scenes. Different from constructing cost vol…
Light Field (LF) images captured under low illumination conditions typically exhibit low quality. Recent learning-based methods for low-light LF enhancement are generally tailored to specific illumination inputs, limiting their performance in real-world scenes. Moreover, how to maintain the inherent…
Continual Multi-Agent Reinforcement Learning (Co-MARL) requires agents to address catastrophic forgetting issues while learning new coordination policies with the dynamics team. In this paper, we delve into the core of Co-MARL, namely Relation Patterns, which refer to agents’ general understanding o
Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context. Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextu…
Vehicle re-identification aims to match vehicles across non-overlapping camera views. Many existing methods extract features from one specific image, and these methods lack view-invariance when comparing vehicles of different orientations. As a result, discriminative parts obscured by viewpoint chan…
Explaining deep models for time-series data is crucial for identifying key patterns in sensitive domains, such as healthcare and finance. However, due to the lack of unified optimization criterion, existing explanation methods often suffer from redundancy and incompleteness, where irrelevant pattern…
Spatiotemporal trajectory data is crucial for various traffic-related applications. However, issues such as device malfunctions and network instability often result in sparse trajectories that lose detailed movement information compared to their dense counterparts. Recovering missing points in spars…
Affective brain-computer interfaces (aBCIs) play a crucial role in personalized human–computer interaction and neurofeedback modulation. To develop practical and effective aBCI paradigms and to investigate the spatial-temporal dynamics of brain activity under emotional inducement, portable electroen…
Spatial-temporal forecasting and imputation are important for real-world intelligent systems. Most existing methods are tailored for individual forecasting or imputation tasks but are not designed for both. Additionally, they are less effective for zero-shot and few-shot learning. While pre-trained…
Spatio-temporal trajectories are crucial for data mining tasks, requiring versatile learning methods that can accurately extract movement patterns and travel purposes. While large language models (LLMs) have shown remarkable versatility through training on extensive datasets, and trajectories share
Vehicle GPS trajectories record how vehicles move over time, storing valuable travel semantics, including movement patterns and travel purposes. Learning travel semantics effectively and efficiently is crucial for real-world applications of trajectory data, which is hindered by two major challenges.…
Vehicle GPS trajectories provide valuable movement information that supports various downstream tasks and applications. A desirable trajectory learning model should be able to transfer across regions and tasks without retraining, avoiding the need to maintain multiple specialized models and subpar p…
The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC assume that traffic data from all surrounding intersections is fully and continuously available through sensors. In real-wo…
In the domain of real-world agents, the application of Reinforcement Learning (RL) remains challenging due to the necessity for safety constraints. Previously, Constrained Reinforcement Learning (CRL) has predominantly focused on on-policy algorithms. Although these algorithms exhibit a degree of ef…
Despite the impressive success in visual control challenges, Visual Reinforcement Learning (VRL) policies have struggled to generalize to other scenarios. Existing works attempt to empirically improve the generalization capability, lacking theoretical support. In this work, we explore how to learn d…
Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users’ intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in…
Due to the recurrent structure of RNN, the long information propagation path poses limitations in capturing long-term dependencies, gradient explosion/vanishing issues, and inefficient sequential execution. Based on this, we propose a novel paradigm called Parallel Gated Network (PGN) as the new suc…
In visual Reinforcement Learning (RL), the challenge of generalization to new environments is paramount. This study pioneers a theoretical analysis of visual RL generalization, establishing an upper bound on the generalization objective, encompassing policy divergence and Bellman error components. M…
A core step of mining human mobility data is to learn accurate representations for user-generated check-in sequences. The learned representations should be able to fully describe the spatial-temporal mobility patterns of users and the high-level semantics of traveling. However, existing check-in seq…
Sleep stage classification based on physiological time-series is essential for sleep quality evaluation and the diagnosis of sleep disorders in clinical practice. Existing machine learning studies have achieved adequate results in sleep stage classification. However, those methods neglect the signif…
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of d…
Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing knowledge in relational digraphs and achieves notable results. However, during r…
Capturing semantic information is crucial for accurate long-range time series forecasting, which involves modeling global and local correlations, as well as discovering long- and short-term repetitive patterns. Previous works have partially addressed these issues separately, but have not been able t…
Electroencephalogram (EEG) signals for motor imagery (MI) are easily influenced by the environment and the state of the subject, which exhibit temporal and spatial variance. And this variance is more significant across subjects and sessions, which imposes limitations on the cross-domain MI tasks. To…
Depth estimation from Light Field (LF) images is a crucial basis for LF related applications. Since multiple views with abundant information are available, how to effectively fuse features of these views is a key point for accurate LF depth estimation. In this paper, we propose a novel attention-bas…
Traffic accident forecasting is of great importance to urban public safety, emergency treatment, and construction planning. However, it is very challenging since traffic accidents are affected by multiple factors, and have multi-scale dependencies on both spatial and temporal dimensional features. M…
Pre-training location embeddings from spatial-temporal trajectories is a fundamental procedure and very beneficial for user next location prediction. In the real world, a location usually has variable functionalities under different contextual environments. If the exact functions of a location in th…
Foreground occlusion removal task aims to automatically detect and remove foreground occlusions and recover background objects. Since for Light Fields (LFs), background objects occluded in some views may be seen in other views, the foreground occlusion removal task for LFs is easy to achieve. In thi…
Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the mult…
Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In particular, owing to the limited knowledge of the human brain, predefining a su…
Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras.…