SeaMo: A Multi-Seasonal and Multimodal Remote Sensing Foundation Model

Remote Sensing (RS) data contains a wealth of multi-dimensional information crucial for Earth observation. Owing to its vast volume, diverse sources, and temporal properties, RS data is highly suitable for the development of large Visual Foundation Models (VFMs). VFMs act as robust feature extractors, learning from extensive RS data, and are subsequently fine-tuned for deployment in various geoscientific tasks. However, current VFMs in the RS domain are predominantly pretrained and tailored exclusively for specific characteristics of RS imagery, neglecting the potential of utilizing the multi-dimensional properties of RS data. Therefore, in this work, we propose SeaMo, a pioneering visual foundation model that integrates multi-seasonal and multimodal information in the RS field. SeaMo is designed to harness multiple properties of RS data. Within the masked image modeling framework, we employ non-aligned cropping techniques to extract spatial properties, use multi-source inputs for multimodal integration, and incorporate temporal-multimodal fusion blocks for effective assimilation of multi-seasonal data. SeaMo explicitly models the multi-dimensional properties of RS data, making the model more comprehensive, robust, and versatile. We applied SeaMo to several downstream geoscience tasks, which demonstrated exceptional performance. Extensive ablation studies were conducted to validate the model's superiority.

A Digitalized Atlas for Pulmonary Airway

In this work, we proposed AirwayAtlas, which is an end-to-end pipeline for automatic extraction of airway anatomies with lobar, segmental and subsegmental labeling. A compact representation, AirwaySign, is generated based on diverse features of airway branches. Experiments on multi-center datasets validated the effectiveness of AirwayAtlas. We also demonstrated that AirwaySign is a powerful tool for correlation analysis on pulmonary diseases.

Airway Labeling Meets Clinical Applications: Reflecting Topology Consistency and Outliers via Learnable Attentions

Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway anatomical labeling is challenging due to significant individual variability and anatomical variations. Previous methods are prone to generate inconsistent predictions, which is harmful for preoperative planning and intraoperative navigation. This paper aims to address these challenges by proposing a novel method that enhances topological consistency and improves the detection of abnormal airway branches. We propose a novel approach incorporating two modules: the Soft Subtree Consistency (SSC) and the Abnormal Branch Saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates the interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous aggregation of features between normal and abnormal nodes. Evaluated on a challenging dataset characterized by severe airway distortion and atrophy, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains a 91.4% accuracy at the segmental level and an 83.7% accuracy at the subsegmental level, representing a 1.4% increase in subsegmental accuracy and a 3.1% increase in topological consistency. Notably, the method demonstrates reliable performance in cases with disease-induced airway deformities, ensuring consistent and accurate labeling.

Efficient Low-Resolution Face Recognition via Bridge Distillation

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, ~934 and 763 faces per second on GPU, CPU and mobile phone, respectively.

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark

To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD) have received significant attention in recent years. Though those two lines of research indeed share the same objective, they have been studied independently in the community due to distinct evaluation setups, creating a gap that hinders the application and evaluation of methods from one to the other. To bridge the gap, in this work, we present a Unified Benchmark for unsupervised Graph-level OOD and anomaly Detection (our method), a comprehensive evaluation framework that unifies GLAD and GLOD under the concept of generalized graph-level OOD detection. Our benchmark encompasses 35 datasets spanning four practical anomaly and OOD detection scenarios, facilitating the comparison of 16 representative GLAD/GLOD methods. We conduct multi-dimensional analyses to explore the effectiveness, generalizability, robustness, and efficiency of existing methods, shedding light on their strengths and limitations. Furthermore, we provide an open-source codebase (https://github.com/UB-GOLD/UB-GOLD) of our method to foster reproducible research and outline potential directions for future investigations based on our insights.

Masked Face Recognition with Generative-to-Discriminative Representations

Masked face recognition is important for social good but challenged by diverse occlusions that cause insufficient or inaccurate representations. In this work, we propose a unified deep network to learn generative-to-discriminative representations for facilitating masked face recognition. To this end, we split the network into three modules and learn them on synthetic masked faces in a greedy module-wise pretraining manner. First, we leverage a generative encoder pretrained for face inpainting and finetune it to represent masked faces into category-aware descriptors. Attribute to the generative encoder's ability in recovering context information, the resulting descriptors can provide occlusion-robust representations for masked faces, mitigating the effect of diverse masks. Then, we incorporate a multi-layer convolutional network as a discriminative reformer and learn it to convert the category-aware descriptors into identity-aware vectors, where the learning is effectively supervised by distilling relation knowledge from off-the-shelf face recognition model. In this way, the discriminative reformer together with the generative encoder serves as the pretrained backbone, providing general and discriminative representations towards masked faces. Finally, we cascade one fully-connected layer following by one softmax layer into a feature classifier and finetune it to identify the reformed identity-aware vectors. Extensive experiments on synthetic and realistic datasets demonstrate the effectiveness of our approach in recognizing masked faces.

A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers

Hyperspectral Image Classification (HSC) is a challenging task due to the high dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine Learning approaches while effective, face challenges in real-world data due to varying optimal feature sets, subjectivity in human-driven design, biases, and limitations. Traditional approaches encounter the curse of dimensionality, struggle with feature selection and extraction, lack spatial information consideration, exhibit limited robustness to noise, face scalability issues, and may not adapt well to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as powerful tools for addressing these challenges. This survey provides a comprehensive overview of the current trends and future prospects in HSC, focusing on the advancements from DL models to the emerging use of Transformers. We review the key concepts, methodologies, and state-of-the-art approaches in DL for HSC. We explore the potential of Transformer-based models in HSC, outlining their benefits and challenges. We also delve into emerging trends in HSC, as well as thorough discussions on Explainable AI and Interoperability concepts along with Diffusion Models (image denoising, feature extraction, and image fusion). Lastly, we address several open challenges and research questions pertinent to HSC. Comprehensive experimental results have been undertaken using three HS datasets to verify the efficacy of various conventional DL models and Transformers. Finally, we outline future research directions and potential applications that can further enhance the accuracy and efficiency of HSC. The Source code is available at \href{https://github.com/mahmad00/Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Survey-2024}{github.com/mahmad00}.

SpectralMamba: Efficient Mamba for Hyperspectral Image Classification

Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences. However, despite the success of these sequential architectures, the non-ignorable inefficiency caused by either difficulty in parallelization or computationally prohibitive attention still hinders their practicality, especially for large-scale observation in remote sensing scenarios. To address this issue, we herein propose SpectralMamba -- a novel state space model incorporated efficient deep learning framework for HS image classification. SpectralMamba features the simplified but adequate modeling of HS data dynamics at two levels. First, in spatial-spectral space, a dynamical mask is learned by efficient convolutions to simultaneously encode spatial regularity and spectral peculiarity, thus attenuating the spectral variability and confusion in discriminative representation learning. Second, the merged spectrum can then be efficiently operated in the hidden state space with all parameters learned input-dependent, yielding selectively focused responses without reliance on redundant attention or imparallelizable recurrence. To explore the room for further computational downsizing, a piece-wise scanning mechanism is employed in-between, transferring approximately continuous spectrum into sequences with squeezed length while maintaining short- and long-term contextual profiles among hundreds of bands. Through extensive experiments on four benchmark HS datasets acquired by satellite-, aircraft-, and UAV-borne imagers, SpectralMamba surprisingly creates promising win-wins from both performance and efficiency perspectives.

Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection

Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from low resolution, simple background, and small size of the detection data. These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter selection. To this end, we build a new set of HAD benchmark datasets for improving the robustness of the HAD algorithm in complex scenarios, AIR-HAD for short. Accordingly, we propose a generalized and interpretable HAD network by deeply unfolding a dictionary-learnable LLR model, named LRR-Net$^+$, which is capable of spectrally decoupling the background structure and object properties in a more generalized fashion and eliminating the bias introduced by vital interference targets concurrently. In addition, LRR-Net$^+$ integrates the solution process of the Alternating Direction Method of Multipliers (ADMM) optimizer with the deep network, guiding its search process and imparting a level of interpretability to parameter optimization. Additionally, the integration of physical models with DL techniques eliminates the need for manual parameter tuning. The manually tuned parameters are seamlessly transformed into trainable parameters for deep neural networks, facilitating a more efficient and automated optimization process. Extensive experiments conducted on the AIR-HAD dataset show the superiority of our LRR-Net$^+$ in terms of detection performance and generalization ability, compared to top-performing rivals. Furthermore, the compilable codes and our AIR-HAD benchmark datasets in this paper will be made available freely and openly at \url{https://sites.google.com/view/danfeng-hong}.

Timer: Transformers for Time Series Analysis at Scale

Deep learning has contributed remarkably to the advancement of time series analysis. Still, deep models can encounter performance bottlenecks in real-world small-sample scenarios, which can be concealed due to the performance saturation with small models on current benchmarks. Meanwhile, large models have demonstrated great powers in these scenarios through large-scale pre-training. Continuous progresses have been achieved as the emergence of large language models, exhibiting unprecedented ability in few-shot generalization, scalability, and task generality, which is however absent in time series models. To change the current practices of training small models on specific datasets from scratch, this paper aims at an early development of large time series models (LTSM). During pre-training, we curate large-scale datasets with up to 1 billion time points, unify heterogeneous time series into single-series sequence (S3) format, and develop the GPT-style architecture toward LTSMs. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task. The outcome of this study is a Time Series Transformer (Timer), that is pre-trained by autoregressive next token prediction on large multi-domain datasets, and is fine-tuned to downstream scenarios with promising abilities as an LTSM.