Canada

Time series forecasting is a crucial task in various domains. Caused by factors such as trends, seasonality, or irregular fluctuations, time series often exhibits non-stationary. It obstructs stable feature propagation through deep layers, disrupts feature distributions, and complicates learning data distribution changes. As a result, many existing models struggle to capture the underlying patterns, leading to degraded forecasting performance. In this study, we tackle the challenge of non-stationarity in time series forecasting with our proposed framework called U-Mixer. By combining Unet and Mixer, U-Mixer effectively captures local temporal dependencies between different patches and channels separately to avoid the influence of distribution variations among channels, and merge low- and high-levels features to obtain comprehensive data representations. The key contribution is a novel stationarity correction method, explicitly restoring data distribution by constraining the difference in stationarity between the data before and after model processing to restore the non-stationarity information, while ensuring the temporal dependencies are preserved. Through extensive experiments on various real-world time series datasets, U-Mixer demonstrates its effectiveness and robustness, and achieves 14.5% and 7.7% improvements over state-of-the-art (SOTA) methods.

AAAI 2024

U-mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting | VIDEO

dmkm: rule mining & pattern mining

ml: calibration & uncertainty quantification

ml: time-series/data streams

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Consistency and interpretability have long been the critical issues in MRI reconstruction. While interpretability has been dramatically improved with the employment of deep unfolding networks (DUNs), current methods still suffer from inconsistencies and generate inferior anatomical structure. Especially in multi-contrast scenes, different imaging protocols often exacerbate the concerned issue. In this paper, we propose a range-null decomposition-assisted DUN architecture to ensure consistency while still providing desirable interpretability. Given the input decomposed, we argue that the inconsistency could be analytically relieved by feeding solely the null-space component into proximal mapping, while leaving the range-space counterpart fixed. More importantly, a correlation decoupling scheme is further proposed to narrow the information gap for multi-contrast fusion, which dynamically borrows isotropic features from the opponent while maintaining the modality-specific ones. Specifically, the two features are attached to different frequencies and learned individually by the newly designed isotropy encoder and anisotropy encoder. The former strives for the contrast-shared information, while the latter serves to capture the contrast-specific features. The quantitative and qualitative results show that our proposal outperforms most cutting-edge methods by a large margin. Codes will be released on https://github.com/chenjiachengzzz/RNU.

Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

Multi-Domain Face Anti-Spoofing (MD-FAS) is a practical setting that aims to update models on new domains using only novel data while ensuring that the knowledge acquired from previous domains is not forgotten.
Prior methods utilize the responses from models to represent the previous domain knowledge or map the different domains into separated feature spaces to prevent forgetting.
However, due to domain gaps, the responses of new data are not as accurate as those of previous data. 
Also, without the supervision of previous data, separated feature spaces might be destroyed by new domains while updating, leading to catastrophic forgetting.
Inspired by the challenges posed by the lack of previous data, we solve this issue from a new standpoint that generates hallucinated previous data for updating FAS model.
To this end, we propose a novel Domain-Hallucinated Updating (DHU) framework to facilitate the hallucination of data.
Specifically, Domain Information Explorer learns representative domain information of the previous domains. 
Then, Domain Information Hallucination module transfers the new domain data to pseudo-previous domain ones.
Moreover, Hallucinated Features Joint Learning module is proposed to asymmetrically align the new and pseudo-previous data for real samples via dual levels to learn more generalized features, promoting the results on all domains.
Our experimental results and visualizations demonstrate that the proposed method outperforms state-of-the-art competitors in terms of effectiveness.

Domain-Hallucinated Updating for Multi-Domain Face Anti-spoofing

Self-training based few-shot node classification (FSNC) methods have shown excellent performance in real applications, but they cannot make the full use of the information in the base set and are easily affected by the quality of pseudo-labels. To address these issues, this paper proposes a new self-training  FSNC method by involving the representation distillation and the pseudo-label distillation. Specifically, the representation distillation includes two knowledge distillation methods (\ie, the local representation distillation and the global representation distillation) to transfer the information in the base set to the novel set. The pseudo-label distillation is designed to conduct knowledge distillation on the pseudo-labels to improve their quality. 
Experimental results showed that our method achieves supreme performance, compared with state-of-the-art methods. Our code and a comprehensive theoretical version are available at https://github.com/zongqianwu/KD-FSNC.

Self-Training Based Few-Shot Node Classification by Knowledge Distillation

Predicting the state evolution of ultra high-dimensional, time-reversible fluid dynamic systems is a crucial but computationally expensive task. Existing physics-informed neural networks either incur high inference cost or cannot preserve the time-reversible nature of the underlying dynamics system. We propose a model-based approach to identify low-dimensional, time reversible, nonlinear fluid dynamic systems. Our method utilizes the symplectic structure of reduced Eulerian fluid and use stochastic Riemann optimization to obtain a low-dimensional bases that minimize the expected trajectory-wise dimension-reduction error over a given distribution of initial conditions. We show that such minimization is well-defined since the reduced trajectories are differentiable with respect to the subspace bases over the entire Grassmannian manifold, under proper choices of timestep sizes and numerical integrators. Finally, we propose a loss function measuring the trajectory-wise discrepancy between the original and reduced models. By tensor precomputation, we show that gradient information of such loss function can be evaluated efficiently over a long trajectory without time-integrating the high-dimensional dynamic system. Through evaluations on a row of simulation benchmarks, we show that our method reduces the discrepancy by 50-90 percent over conventional reduced models and we outperform PINNs by exactly preserving the time reversibility.

Learning Reduced Fluid Dynamics

This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Normal Distance to address this issue. This not only mitigates the challenge but also facilitates network training and substantially enhances the network robustness against noise. Subsequently, we devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion. This design empowers the network to capture intricate geometric details more effectively and alleviate the ambiguity in scale selection. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on both synthetic and real-world datasets, particularly in scenarios contaminated by noise. Our implementation is available at https://github.com/YingruiWoo/CMG-Net_Pytorch.

CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-Scale Geometry | VIDEO

Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors.
Many successful works have utilized mixture models which require identification of positive mixture components, and correspondingly fall into two main lines: prediction-based and anchor-based matching.
The prediction clustering phenomenon in prediction-based matching makes it difficult to pick representative trajectories for downstream tasks, while the anchor-based matching suffers from a limited regression capability.
In this paper, we introduce a novel paradigm, named Evolving and Distinct Anchors (EDA), to define the positive and negative components for multimodal motion prediction based on mixture models.
We enable anchors to evolve and redistribute themselves under specific scenes for an enlarged regression capacity.
Furthermore, we select distinct anchors before matching them with the ground truth, which results in impressive scoring performance.
Our approach enhances all metrics compared to the baseline MTR, particularly with a notable relative reduction of 13.5% in Miss Rate, resulting in state-of-the-art performance on the Waymo Open Motion Dataset.
Appendix and code are available at https://github.com/Longzhong-Lin/EDA.

EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction | VIDEO

Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention. Despite the fruitful efforts of previous models in temporal KGQA, they still have several limitations. (I) They adopt  pre-trained language models (PLMs) to obtain question representations, while PLMs tend to focus on entity information and ignore entity transfer caused by temporal constraints, and finally fail to learn specific temporal representations of entities. (II) They neither emphasize the graph structure between entities nor explicitly model the multi-hop relationship in the graph, which will make it difficult to solve complex multi-hop question answering. To alleviate this problem, we propose a novel Question Calibration and Multi-Hop Modeling (QC-MHM) network. Specifically, We first calibrate the question representation by fusing the question and the time-constrained concepts in KG. Then, we construct the GNN layer to complete multi-hop message passing. Finally, the question representation is combined with the embedding output by the GNN to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of QC-MHM on the CronQuestions dataset's complex questions are absolutely improved by 5.1% and 1.2% compared to the best-performing baseline.  Moreover, QC-MHM can generate interpretable and trustworthy predictions.

Question Calibration and Multi-Hop Modeling for Temporal Question Answering | VIDEO

Existing super-resolution methods exhibit limitations when applied to nighttime scenes, primarily due to their lack of adaptation to low-pair dynamic range and noise-heavy dark-light images. In response, this research introduces an innovative customized framework to simultaneously Brighten and Zoom in low-resolution images captured in low-light conditions, dubbed BrZoNet. The core method begins by feeding low-light, low-resolution images, and their corresponding ground truths into the Retinex-induced siamese decoupling network. This process yields distinct reflectance maps and illuminance maps, guided by supervision from the ground truth’s decomposition maps. Subsequently, these reflectance and illuminance maps transition into an intricate super-resolution sub-network. This sub-network employs a meticulously designed cross-layer content-aware interactor - Illumination-aware Interaction Unit(IaIU), elegantly endowed with a gating mechanism. The IaIU facilitates meaningful feature interaction between illuminance and reflectance features while effectively reducing unwanted noise. An intricate super-resolution cage is also constructed to comprehensively integrate information, ultimately resulting in the generation of high-resolution images featuring intricate details. Thorough and diverse experiments validate the superiority of the proposed BrZoNet, surpassing contemporary cutting-edge technologies by proficiently augmenting brightness and intricately recovering complex details, showcasing advancements of 7.1% in PSNR, 2.4% in SSIM, and an impressive 36.8% in LPIPS metrics.

Unveiling Details in the Dark: Simultaneous Brightening and Zooming for Low-Light Image Enhancement

Point cloud registration, a fundamental task in 3D computer vision, has remained largely unexplored in cross-source point clouds and unstructured scenes. The primary challenges arise from noise, outliers, and variations in scale and density. However, neglected geometric natures of point clouds restricts the performance of current methods. In this paper, we propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds, facilitating robust capture of geometric intricacy. Specifically, we design the Skeleton Extraction Module to extract skeleton points and skeletal features in an unsupervised manner, which is inherently robust to noise and density variances. Then, we propose the Skeleton-Aware GeoTransformer to encode high-level skeleton-aware features. It explicitly captures the topological natures and inter-point-cloud skeletal correlations with the noise-robust and density-invariant skeletal representations. Next, we introduce the Correspondence Dual-Sampler to facilitate correspondences by augmenting the correspondence set with skeletal correspondences. Furthermore, we construct a challenging novel large-scale cross-source point cloud dataset named KITTI CrossSource for benchmarking cross-source point cloud registration methods. Extensive quantitative and qualitative experiments are conducted to demonstrate our approach’s superiority and robustness on both cross-source and same-source datasets. To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.

SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration

As an essential computer vision task, Continual Semantic Segmentation (CSS) has received a lot of attention. However, security issues regarding this task have not been fully studied. To bridge this gap, we study the problem of attacks in CSS in this paper. We first propose a new task, namely, attacks on incremental samples in CSS, and reveal that the attacks on incremental samples corrupt the performance of CSS in both old and new classes.  Moreover, we present an adversarial sample generation method based on class shift, namely Class Shift Attack (CS-Attack), which is an offline and easy-to-implement approach for CSS. CS-Attack is able to significantly degrade the performance of models on both old and new classes without knowledge of the incremental learning approach, which undermines the original purpose of the incremental learning, i.e., learning new classes while retaining old knowledge. Experiments show that on the popular datasets Pascal VOC, ADE20k, and Cityscapes, our approach easily degrades the performance of currently popular CSS methods, which reveals the importance of security in CSS.

U-mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting | VIDEO

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Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

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U-mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting | VIDEO

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Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

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RESOURCES

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