Spiking Neural Networks (SNNs), models inspired by neural mechanisms in the brain, allow for energy-efficient implementation on neuromorphic hardware. However, SNNs trained with current direct training approaches are constrained to a specific time step. This "temporal inflexibility" 1) hinders SNNs' deployment on time-step-free fully event-driven chips and 2) prevents energy-performance balance based on dynamic inference time steps. In this study, we first explore the feasibility of training SNNs that generalize across different time steps. We then introduce Mixed Time-step Training (MTT), a novel method that improves the temporal flexibility of SNNs, making SNNs adaptive to diverse temporal structures. During each iteration of MTT, random time steps are assigned to different SNN stages, with spikes transmitted between stages via communication modules. After training, the weights are deployed and evaluated on both time-stepped and fully event-driven platforms. Experimental results show that models trained by MTT gain remarkable temporal flexibility, friendliness for both event-driven and clock-driven deployment (nearly lossless on N-MNIST and 10.1\% higher than standard methods on CIFAR10-DVS), enhanced network generalization, and near SOTA performance. To the best of our knowledge, this is the first work to report the results of large-scale SNN deployment on fully event-driven scenarios.

Source-Free Domain Adaptive Object Detection (SFDA-OD) aims to apply a pre-trained detector from the source domain to unlabeled data in the target domain. Existing methods typically utilize techniques such as domain alignment and fine-tuning model parameters to achieve satisfactory performance in the target domain. However, even using SFDA approaches, the accuracy of the model on the target domain remains significantly accuracy gap compared to the fully-supervised model. In this paper, we explore a new scenario Source-Free Active Domain Adaptive Object Detection (SFADA-OD) to better enhance model adaptability and performance within limited annotation, and propose a novel approach named Learning Domain Distance for Active Pick (LDDAP). Firstly, we design graph-aware distance learning to calculate the distance between the encoded images and the predicted instances, which better evaluate the distance between domains in SFDA scenario. Secondly, we propose instance-aware active sampling to facilitate instance-level distance associated with other indexes to select the most valuable images, thereby maximizing the effectiveness of the limited labeled information. The results demonstrate a significant enhancement in performance over baseline cross-domain models and other Active Learning related methods on several well-known public datasets.In addition, our model even surpasses fully-supervised results on the dataset Pascal → Watercolor. Code is avaliable at LDDAP

Alzheimer's disease (AD) is an incurable neurodegenerative disorder characterized by progressive cognitive and functional decline. Consequently, early diagnosis and accurate prediction of disease progression are of paramount importance and inherently complex, necessitating the integration of multi-modal data. While existing methods are typically task-specific and lack generalization, we present ADFound, the first multi-modal foundation model for AD capable of simultaneously addressing diagnosis and prognosis tasks through a unified framework. ADFound leverages a substantial amount of unlabeled 3D multi-modal neuroimaging, including paired and unpaired data, to achieve its objectives. Specifically, ADFound is developed upon the Multi-modal Vim encoder by Vision Mamba block to capture long-range dependencies inherent in 3D multi-modal medical images. To efficiently pre-train ADFound on unlabeled paired and upaired multi-modal neuroimaging data, we proposed a novel self-supervised learning framework that integrates multi-modal masked autoencoder (MAE) and contrastive learning. The multi-modal MAE aims to learn local relations among modalities by reconstructing images with unmasked image patches. Additionally, we introduce a Dual Contrastive Learning for Multi-modal Data to enhance the discriminative capabilities of multi-modal representations from intra-modal and inter-modal perspectives. Our experiments demonstrate that ADFound outperforms state-of-the-art methods across a wide range of downstream tasks relevant to the diagnosis and prognosis of AD. Furthermore, the results indicate that our foundation model can be extended to more modalities, such as non-image data, showing its versatility.

Efficient and timely assessment of road network dynamic changes is crucial for the comprehensive evaluation of urban development, transportation accessibility, and environmental impacts. While existing methods mainly focus on optimizing performance with very-high-resolution remote sensing images on public road datasets, their practical applicability remains to unlock when confronted with large-scale real-world applications utilizing multi-spectral remote sensing images. The limitations manifest in unsatisfactory model generalization and fragmented segmentation, reducing the effectiveness of road extraction outcomes. To address these challenges, this study introduces a novel connectivity-aware approach tailored to address road extraction challenges in real-world scenarios. Leveraging Sentinel-2 multi-spectral imagery, this study conducts a 6-year road change mapping over an expansive 10,097 square kilometers in Xi’an, China. Experimental and evaluation results underscore the efficacy of the proposed methodology for widespread applications in urban planning and environmental management, offering a robust solution for practical and efficient road extraction in diverse and extensive large-scale urban investigation.

Spiking neural networks (SNNs), inspired by biological processes, use spike signals for inter-layer communication, presenting an energy-efficient alternative to traditional neural networks. To realize the theoretical advantages of SNNs in energy efficiency, it is essential to deploy them onto neuromorphic chips. On clock-driven synchronous chips, employing shorter time steps can enhance energy efficiency but reduce SNN performance. Compared to the clock-driven synchronous chip, the event-driven asynchronous chip achieves much lower energy consumption but only supports some specific network operations. Recently, a series of SNN projects have achieved tremendous success, significantly improving the SNN's performance. However, event-driven asynchronous chips do not support some of the proposed structures, making it impossible to integrate these SNNs into asynchronous hardware. In response to these problems, we propose the Spiking Token Mixer (STMixer) architecture, which consists exclusively of operations supported by asynchronous scenarios, including convolutional, fully connected layers and residual paths. Our series of experiments also demonstrates that STMixer achieves performance on par with spiking transformers in synchronous scenarios with very low timesteps. This indicates its ability to achieve the same level of performance with lower power consumption in synchronous scenarios. The codes are available at \url{https://github.com/brain-intelligence-lab/STMixer_demo}.