DiM-TS: Bridge the Gap between Selective State Space Models and Time Series for Generative Modeling

A recent study introduced the Influence Score, a controllability-based metric for analyzing state-space models (SSMs) like Mamba. This metric quantifies the impact of tokens on subsequent states and outputs, evaluated across various Mamba variants through multiple experiments. The findings reveal that the Influence Score correlates with model size and training data, indicating a deeper understanding of Mamba's internal dynamics compared to attention-based models.

A novel self-supervised learning framework named ConMamba has been proposed for plant disease detection, addressing the limitations of existing deep learning methods that require extensive annotated datasets. ConMamba utilizes the Vision Mamba Encoder, which incorporates a bidirectional State Space Model to efficiently capture long-range dependencies in visual data.

A comparative study has been conducted on UNet-based architectures for liver tumor segmentation in multi-phase contrast-enhanced computed tomography (CECT), revealing that ResNet-based models consistently outperform Transformer and Mamba-based alternatives. The study also highlights the effectiveness of integrating attention mechanisms, particularly the Convolutional Block Attention Module (CBAM), in enhancing segmentation quality.

CADTrack introduces a novel framework for RGB-Thermal tracking, addressing the challenges of modality discrepancies that hinder effective feature representation and tracking accuracy. The framework employs Mamba-based Feature Interaction and a Contextual Aggregation Module to enhance feature discrimination and reduce computational costs.

A new framework named SAMBA has been introduced to enhance long-sequence electroencephalogram (EEG) modeling, addressing the challenges posed by high sampling rates and extended recording durations. This self-supervised learning model utilizes a Mamba-based U-shaped encoder-decoder architecture to effectively capture long-range temporal dependencies and spatial variability in EEG data.

A new dataset titled 'BCWildfire' has been introduced, providing a comprehensive 25-year daily-resolution record of wildfire risk across 240 million hectares in British Columbia. This dataset includes 38 covariates such as active fire detections, weather variables, fuel conditions, terrain features, and human activity, addressing the scarcity of publicly available benchmark datasets for wildfire risk prediction.

A novel hybrid architecture named HyM-UNet has been proposed to enhance medical image segmentation by combining the local feature extraction strengths of Convolutional Neural Networks (CNNs) with the global modeling capabilities of Mamba. This architecture employs a Hierarchical Encoder and a Mamba-Guided Fusion Skip Connection to effectively bridge local and global features, addressing the limitations of traditional CNNs in capturing complex anatomical structures.

MambaX has been introduced as a nonlinear state predictive control model aimed at enhancing image super-resolution (SR) by mapping consecutive spectral bands into a latent state space. This model addresses limitations in existing approaches that primarily focus on resolution enhancement without managing error propagation during the reconstruction process.