United States

Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements. Estimating the source locations and their extended area is fundamental in neuroscience and clinical applications. 
However, the estimation is challenging because of the ill-posedness property and low signal-to-noise (SNR) level in the EEG/MEG data. In this work, a combinatorial search framework is proposed to solve the ESI problem with a provable optimality guarantee by exploiting the graph neighborhood information in the brain source space. Specifically, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A* to solve it. The proposed algorithm is guaranteed to produce an optimal solution to the ESI problem.  Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the electrical source activation in the brain.

AAAI 2023

Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality

combinatorial search

brain source imaging

neuroscience

healthcare

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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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.

poster

The model identifiability is a considerable issue in the unsupervised learning of disentangled representations. The PCA inductive biases revealed recently for unsupervised disentangling in VAE-based models are shown to improve local alignment of latent dimensions with principal components of the data. In this paper, in additional to the PCA inductive biases, we propose novel geometric inductive biases from the manifold perspective for unsupervised disentangling, which induce the model to capture the global geometric properties of the data manifold with guaranteed model identifiability. We also propose a Geometric Disentangling Regularized AutoEncoder (GDRAE) that combines the PCA and the proposed geometric inductive biases in one unified framework. The experimental results show the usefulness of the geometric inductive biases in unsupervised disentangling and the effectiveness of our GDRAE in capturing the geometric inductive biases.

Geometric Inductive Biases for Identifiable Unsupervised Learning of Disentangled Representations

Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or optical flow constraints. Moreover, video correspondence predictive models require mining more inherent properties in videos, such as structural information. In this work, we propose the VideoHiGraph, a space-time correspondence framework based on a learnable graph kernel. Concerning the video as the spatial-temporal graph, the learning objectives of VideoHiGraph are emanated in a self-supervised manner for predicting unobserved hidden graphs via graph kernel manner. We learn a representation of the temporal coherence across frames in which pairwise similarity defines the structured hidden graph, such that a biased random walk graph kernel along the sub-graph can predict long-range correspondence. Then, we learn a refined representation across frames on the node-level via a dense graph kernel. The self-supervision of the model training is formed by the structural and temporal consistency of the graph. VideoHiGraph achieves superior performance and demonstrates its robustness across the benchmark of label propagation tasks involving objects, semantic parts, keypoints, and instances. Our algorithm implementations have been made publicly available at https://github.com/zyqin19/VideoHiGraph.

Exposing the Self-supervised Space-Time Correspondence Learning via Graph Kernels

Multi-person pose estimation (MPPE) has achieved impressive progress in recent years. However, due to the large variance of appearances among images or occlusions, the model can hardly learn consistent patterns enough, which leads to severe location jitter and missing issues. In this study, we propose a novel framework, termed \textbf{I}nter-image \textbf{Con}trastive consistency (ICON), to strengthen the keypoint consistency among images for MPPE. Concretely, we consider two-fold consistency constraints, which include single keypoint constrastive consistency (SKCC) and pair relation contrastive consistency (PRCC).  The SKCC learns to strengthen the consistency of individual keypoints across images in the same category to improve the category-specific robustness.
Only with SKCC, the model can effectively reduce location errors caused by large appearance variations, but remains challenging with extreme postures (e.g., occlusions) due to lack of relational guidance.  Therefore, PRCC is proposed to strengthen the consistency of pair-wise joint relation between images to preserve the instructive relation. Cooperating with SKCC, PRCC further improves structure aware robustness in handling extreme postures. Extensive experiments on kinds of architectures across three datasets (i.e., MS-COCO, MPII, CrowdPose) show the proposed ICON achieves substantial improvements over baselines. Furthermore, ICON under the semi-supervised setup can obtain comparable results with the fully-supervised methods using only 30% labeled data. 

Inter-Image Contrastive Consistency for Multi-Person Pose Estimation

One of the most challenges for anomaly detection (AD) is
how to learn one unified and generalizable model to adapt
to multi-class especially cross-class settings: the model is
trained with normal samples from seen classes with the
objective to detect anomalies from both seen and unseen
classes. In this work, we propose a novel Proposal Masked
Anomaly Detection (termed as PMAD) approach for such
challenging multi- and cross-class anomaly detection. The
proposed PMAD can be adapted to seen and unseen classes
by two key designs: MAE-based patch-level reconstruction
and prototype-guided proposal masking. First, motivated by
MAE (Masked AutoEncoder), we develop a patch-level reconstruction
model rather than the image-level reconstruction
adopted in most AD methods for this reason: the masked
patches in unseen classes can be reconstructed well by using
the visible patches and the adaptive reconstruction capability
of MAE. Moreover, we improve MAE by ViT encoderdecoder
architecture, combinational masking, and visual tokens
as reconstruction objectives to make it more suitable for
anomaly detection. Second, we develop a two-stage anomaly
detection manner during inference. In the proposal masking
stage, the prototype-guided proposal masking module is utilized
to generate proposals for suspicious anomalies as much
as possible, then masked patches can be generated from the
proposal regions. By masking most likely anomalous patches,
the “shortcut reconstruction” issue (i.e., anomalous regions
can be well reconstructed) can be mostly avoided. In the
reconstruction stage, these masked patches are then reconstructed
by the trained patch-level reconstruction model to
determine if they are anomalies. Extensive experiments show
that the proposed PMAD can outperform current state-of-theart
models significantly under the multi- and especially crossclass
settings.

One-for-All: Proposal Masked Cross-Class Anomaly Detection

Most skeleton-based action recognition methods assume that the same type of action samples in the training set and the test set share similar motion patterns. However, action samples in real scenarios usually contain novel motion patterns which are not involved in the training set. As it is laborious to collect sufficient training samples to enumerate various types of novel motion patterns, this paper presents a practical skeleton-based action recognition task where the training set contains common motion patterns of action samples and the test set contains action samples that suffer from novel motion patterns. For this task, we present a Mask Graph Convolutional Network (Mask-GCN) to focus on learning action-specific skeleton joints that mainly convey action information meanwhile masking action-agnostic skeleton joints that convey rare action information and suffer more from novel motion patterns. Specifically, we design a policy network to learn layer-wise body masks to construct masked adjacency matrices, which guide a GCN-based backbone to learn stable yet informative action features from dynamic graph structure. Extensive experiments on our newly collected dataset verify that Mask-GCN outperforms most GCN-based methods when testing with various novel motion patterns.

Novel Motion Patterns Matter for Practical Skeleton-based Action Recognition

Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D de novo generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances, especially when generating large molecules. At the same time, the generated molecules lack enough diversity. This paper proposes a novel diffusion model to address those two challenges. 

First, interatomic relations are not in molecules' 3D point cloud representations. Thus, it is difficult for existing generative models to capture the potential interatomic forces and abundant local constraints.  To tackle this challenge, we propose to augment the potential interatomic forces and further involve dual equivariant encoders to encode interatomic forces of different strengths. Second, existing diffusion-based models essentially shift elements in geometry along the gradient of data density. Such a process lacks enough exploration in the intermediate steps of the Langevin dynamics. To address this issue, we introduce a distributional controlling variable in each diffusion/reverse step to enforce thorough explorations and further improve generation diversity.

Extensive experiments on multiple benchmarks demonstrate that the proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks. We also conduct case studies to help understand the physicochemical properties of the generated molecules.

MDM: Molecular Diffusion Model for 3D Molecule Generation

Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two common challenges afflicting the task are the volatility of the predictions and their computational complexity. We introduce N-HiTS, a model which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. These techniques enable the proposed method to assemble its predictions sequentially, emphasizing components with different frequencies and scales while decomposing the input signal and synthesizing the forecast. We prove that the hierarchical interpolation technique can efficiently approximate arbitrarily long horizons in the presence of smoothness. Additionally, we conduct extensive large-scale dataset experiments from the long-horizon forecasting literature, demonstrating the advantages of our method over the state-of-the-art methods, where N-HiTS provides an average accuracy improvement of almost 20% over the latest Transformer architectures while reducing the computation time by an order of magnitude (50 times). 

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

Transformer and its variants achieve excellent results in various computer vision and natural language processing tasks,  but high computational costs and reliance on large training datasets restrict their deployment in resource-constrained settings. Low-rank approximation of model weights has been effective in compressing CNN models, but its application to transformers has been less explored and is less effective. Existing methods require the complete dataset to fine-tune compressed models, which are both time-consuming and data-hungry. This paper reveals that the features (i.e., activations) are low-rank, but model weights are surprisingly not low-rank. Hence, AAFM is proposed, which adaptively determines the compressed model structure and locally compresses each linear layer's output features rather than the model weights. A second stage, GFM, optimizes the entire compressed network holistically. Both AAFM and GFM only use few training samples without labels, that is, they are few-shot, unsupervised, fast and effective. For example, with only 2K images without labels, 33% of the parameters are removed in DeiT-B with 18.8% relative throughput increase, but only a 0.23% accuracy loss for ImageNet recognition. The proposed methods are successfully applied to the language modeling task in NLP, too. Besides, the few-shot compressed models generalize well in downstream tasks.

Compressing Transformers: Features Are Low-Rank, but Weights Are Not!

Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow boundaries as well as inconsistent illumination between shadow and non-shadow regions. It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions. In this work, we first propose a Retinex-based shadow model, from which we derive a novel transformer-based network, dubbed \textit{ShandowFormer}, to exploit non-shadow regions to help shadow region restoration. A multi-scale channel attention framework is employed to hierarchically capture the global information. Based on that, we propose a Shadow-Interaction Module (SIM) with Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model the context correlation between shadow and non-shadow regions. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, 
to evaluate the proposed method. Our method achieves state-of-the-art performance by using up to $\mathbf{150\times}$ fewer model parameters.



ShadowFormer: Global Context Helps Shadow Removal

Low-light images suffer severe degradation of low lightness and noise corruption, causing unsatisfactory visual quality and visual recognition performance. To solve this problem while meeting the unavailability of paired datasets in widerange scenarios, unsupervised low-light image enhancement (ULLIE) techniques have been developed. However, these  methods are primarily guided to alleviate the degradation effect on visual quality rather than semantic levels, hence limiting their performance in visual recognition tasks. To this end, we propose to learn a Semantic Degradation-Aware Guidance (SDAG) that perceives the low-light degradation effect on semantic levels in a self-supervised manner, which is further
utilized to guide the ULLIE methods. The proposed SDAG utilizes the low-light degradation factors as augmented signals to degrade the low-light images, and then capture their degradation effect on semantic levels. Specifically, our SDAG
employs the subsequent pre-trained recognition model extractor to extract semantic representations, and then learns to self-reconstruct the enhanced low-light image and its augmented degraded images. By constraining the relative reconstruction effect between the original enhanced image and the augmented formats, our SDAG learns to be aware of the degradation effect on semantic levels in a relative comparison manner. Moreover, our SDAG is general and can be plugged into the training paradigm of the existing ULLIE methods. Extensive experiments demonstrate its effectiveness for improving the ULLIE approaches on the downstream recognition tasks while maintaining a competitive visual quality.

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