Spatio-Temporal-Decoupled Masked Pre-training for Traffic Forecasting

Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns. To address this, we propose Spatio-Temporal-Decoupled Masked Pre-training (STD-MAE), a novel framework that employs masked autoencoders to learn and encode complex spatio-temporal dependencies via pre-training. Specifically, we use two decoupled masked autoencoders to reconstruct the traffic data along spatial and temporal axes using a self-supervised pre-training approach. These mask reconstruction mechanisms capture the long-range correlations in space and time separately. The learned hidden representations are then used to augment the downstream spatio-temporal traffic predictor. A series of quantitative and qualitative evaluations on four widely-used traffic benchmarks (PEMS03, PEMS04, PEMS07, and PEMS08) are conducted to verify the state-of-the-art performance, with STD-MAE explicitly enhancing the downstream spatio-temporal models' ability to capture long-range intricate spatial and temporal patterns. Codes are available at https://github.com/Jimmy-7664/STD_MAE.

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

Deep graph kernel point processes

Point process models are widely used to analyze asynchronous events occurring within a graph that reflect how different types of events influence one another. Predicting future events' times and types is a crucial task, and the size and topology of the graph add to the challenge of the problem. Recent neural point process models unveil the possibility of capturing intricate inter-event-category dependencies. However, such methods utilize an unfiltered history of events, including all event categories in the intensity computation for each target event type. In this work, we propose a graph point process method where event interactions occur based on a latent graph topology. The corresponding undirected graph has nodes representing event categories and edges indicating potential contribution relationships. We then develop a novel deep graph kernel to characterize the triggering and inhibiting effects between events. The intrinsic influence structures are incorporated via the graph neural network (GNN) model used to represent the learnable kernel. The computational efficiency of the GNN approach allows our model to scale to large graphs. Comprehensive experiments on synthetic and real-world data show the superior performance of our approach against the state-of-the-art methods in predicting future events and uncovering the relational structure among data.

Conditional Generative Modeling is All You Need for Marked Temporal Point Processes

Recent advancements in generative modeling have made it possible to generate high-quality content from context information, but a key question remains: how to teach models to know when to generate content? To answer this question, this study proposes a novel event generative model that draws its statistical intuition from marked temporal point processes, and offers a clean, flexible, and computationally efficient solution for a wide range of applications involving multi-dimensional marks. We aim to capture the distribution of the point process without explicitly specifying the conditional intensity or probability density. Instead, we use a conditional generator that takes the history of events as input and generates the high-quality subsequent event that is likely to occur given the prior observations. The proposed framework offers a host of benefits, including exceptional efficiency in learning the model and generating samples, as well as considerable representational power to capture intricate dynamics in multi- or even high-dimensional event space. Our numerical results demonstrate superior performance compared to other state-of-the-art baselines.

Spatio-temporal point processes with deep non-stationary kernels

Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent neural network (RNN) models for point process data, they may not successfully capture sophisticated non-stationary dependencies in the data due to their recurrent structures. Another popular type of deep model for point process data is based on representing the influence kernel (rather than the intensity function) by neural networks. We take the latter approach and develop a new deep non-stationary influence kernel that can model non-stationary spatio-temporal point processes. The main idea is to approximate the influence kernel with a novel and general low-rank decomposition, enabling efficient representation through deep neural networks and computational efficiency and better performance. We also take a new approach to maintain the non-negativity constraint of the conditional intensity by introducing a log-barrier penalty. We demonstrate our proposed method's good performance and computational efficiency compared with the state-of-the-art on simulated and real data.

Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation

Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods.

Total Scale: Face-to-Body Detail Reconstruction from Sparse RGBD Sensors

While the 3D human reconstruction methods using Pixel-aligned implicit function (PIFu) develop fast, we observe that the quality of reconstructed details is still not satisfactory. Flat facial surfaces frequently occur in the PIFu-based reconstruction results. To this end, we propose a two-scale PIFu representation to enhance the quality of the reconstructed facial details. Specifically, we utilize two MLPs to separately represent the PIFus for the face and human body. An MLP dedicated to the reconstruction of 3D faces can increase the network capacity and reduce the difficulty of the reconstruction of facial details as in the previous one-scale PIFu representation. To remedy the topology error, we leverage 3 RGBD sensors to capture multiview RGBD data as the input to the network, a sparse, lightweight capture setting. Since the depth noise severely influences the reconstruction results, we design a depth refinement module to reduce the noise of the raw depths under the guidance of the input RGB images. We also propose an adaptive fusion scheme to fuse the predicted occupancy field of the body and face to eliminate the discontinuity artifact at their boundaries. Experiments demonstrate the effectiveness of our approach in reconstructing vivid facial details and deforming body shapes, and verify its superiority over state-of-the-art methods.

Location-aware Single Image Reflection Removal

This paper proposes a novel location-aware deep learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells whether a region is reflection-dominated or transmission-dominated. The novelty is that we use the reflection confidence map as the cues for the network to learn how to encode the reflection information adaptively and control the feature flow when predicting reflection and transmission layers. The integration of location information into the network significantly improves the quality of reflection removal results. Besides, a set of learnable Laplacian kernel parameters is introduced to facilitate the extraction of discriminative Laplacian features for reflection detection. We design our network as a recurrent network to progressively refine each iteration's reflection removal results. Extensive experiments verify the superior performance of the proposed method over state-of-the-art approaches.