Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However, several challenges persist 1) RNNs struggle with centric feature aggregation and are sensitive to interfering pixels, 2) Transformers require significant computational resources and often underperform with limited HSI training samples, and 3) Current scanning methods for converting images into sequence-data are simplistic and inefficient. In response, this study introduces the innovative Mamba-in-Mamba (MiM) architecture for HSI classification, the first attempt of deploying State Space Model (SSM) in this task. The MiM model includes 1) A novel centralized Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2) A Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), a Semantic Token Learner (STL), and a Semantic Token Fuser (STF) for enhanced feature generation and concentration, and 3) A Weighted MCS Fusion (WMF) module coupled with a Multi-Scale Loss Design to improve decoding efficiency. Experimental results from three public HSI datasets with fixed and disjoint training-testing samples demonstrate that our method outperforms existing baselines and state-of-the-art approaches, highlighting its efficacy and potential in HSI applications.

Rethinking ResNets: Improved Stacking Strategies With High Order Schemes

Various Deep Neural Network architectures are keeping massive vital records in computer vision. While drawing attention worldwide, the design of the overall structure somehow lacks general guidance. Based on the relationship between DNN design with numerical differential equations, which several researchers observed in recent years, we perform a fair comparison of residual design with higher-order perspectives. We show that the widely used DNN design strategy, constantly stacking a small design, could be easily improved, supported by solid theoretical knowledge and no extra parameters needed. We reorganize the residual design in higher-order ways, which is inspired by the observation that many effective networks could be interpreted as different numerical discretizations of differential equations. The design of ResNet follows a relatively simple scheme which is Euler forward; however, the situation is getting complicated rapidly while stacking. We suppose stacked ResNet is somehow equalled to a higher order scheme, then the current way of forwarding propagation might be relatively weak compared with a typical high-order method like Runge-Kutta. We propose higher order ResNet to verify the hypothesis on widely used CV benchmarks with sufficient experiments. Stable and noticeable rises in performance are observed, convergence and robustness are benefited.

Learning Gaussian Graphical Models Using Discriminated Hub Graphical Lasso

We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are known, we can always estimate the precision matrix better via DHGL than HGL. When no hubs are known, we use Graphical Lasso (GL) to provide information of hubs and find that the performance of DHGL will always be better than HGL if correct prior information is given and will seldom degenerate when the prior information is wrong.