Cross-modality object detection aims to fuse complementary information from different modalities to improve model performance, which achieves a wider range of applications. However, traditional cross-modality fusion methods, based on CNN or Transformer, inadequately address the issue of pseudo-target information, which causes model attention dispersion to degrade object detection performance. In this paper, we investigate a novel cross-modality fusion approach by associating cross-modal features in a hidden state space based on an improved Mamba with a gating attention mechanism. We propose the Fusion-Mamba Block (FMB), designed to map cross-modal features into a hidden state space for interaction, thereby refining the model's attention on true target areas and enhancing overall performance. The FMB comprises two key modules: State Space Channel Swapping (SSCS) module, which facilitates the fusion of shallow features, and Dual State Space Fusion (DSSF) module, which enables deep fusion and effectively suppresses pseudo-target information within the hidden state space. Our proposed method outperforms state-of-the-art approaches, achieving improvements of 5.9%, 3.5% and 2.1% mAP on $M^{3}$FD, DroneVehicle and FLIR-Aligned, respectively. To the best of our knowledge, this work establishes a new baseline for cross-modality object detection, providing a robust foundation for future research in this area.

In this letter, an alternative data-driven HSI classification model based on CapsNet is proposed rather than recently predominant convolutional neural network (CNN)-based models. To adjust the CapsNet to HSI classification, we tune a new CapsNet architecture with three convolutional layers. The added shallow layer provides higher level features to the primary capsules, which indirectly speeds up the following routing procedure. To guarantee a good convergence of the whole CapsNet, the three shallow layers are initialized by transferring convolutional parameters from a pretrained CNN model. The improved CapsNet-based models with and without vote strategy both achieve significantly superior performance in HSI classification to the state-of-the-art CNN-based methods on real hyperspectral data sets.

Supported by deep learning techniques, remote sensing scene classification, a fundamental task in remote image analysis, has recently obtained remarkable progress. However, due to the severe uncertainty and perturbation within an image, it is still a challenging task and remains many unsolved problems. In this article, we note that regular one-hot labels cannot precisely describe remote sensing images, and they fail to provide enough information for supervision and limiting the discriminative feature learning of the network. To solve this problem, we propose a variational self-distillation network (VSDNet), in which the class entanglement information from the prediction vector acts as the supplement to the category information. Then, the exploited information is hierarchically distilled from the deep layers into the shallow parts via a variational knowledge transfer (VKT) module. Notably, the VKT module performs knowledge distillation in a probabilistic way through variational estimation, which enables end-to-end optimization for mutual information and promotes robustness to uncertainty within the image. Extensive experiments on four challenging remote sensing datasets demonstrate that, with a negligible parameter increase, the proposed VSDNet brings a significant performance improvement over different backbone networks and delivers state-of-the-art results.

Band selection has become an effective method to reduce hyperspectral dimensionality. In this letter, an information-assisted density peak index (IaDPI) is proposed to prioritize the bands. Based on a clustering method by finding density peaks, IaDPI introduces the intraband information entropy into the local density and intercluster distance to ensure cluster centers with a high quality. Also, the band distance is integrated with channel proximity to control the compactness of local density. Owing to the intraband entropy and the interband weighted dissimilarity, the selected band set with top-ranked IaDPI scores can hold high local density, clear global distinction, and good informative quality. Experimental results on real hyperspectral data indicate the advantages of the proposed IaDPI in good selection quality, robust noise immunity, and high classification accuracy.

Unsupervised monocular depth estimation plays a vital role for endoscopy-based minimally invasive surgery (MIS). However, it remains challenging due to the distinctive imaging characteristics of endoscopy which disrupt the assumption of photometric consistency, a foundation relied upon by conventional methods. Distinct from recent approaches taking image pre-processing strategy, this paper introduces a pioneering solution through intrinsic image decomposition (IID) theory. Specifically, we propose a novel end-to-end intrinsic-based unsupervised monocular depth learning framework that is comprised of an image intrinsic decomposition module and a synthesis reconstruction module. This framework seamlessly integrates IID with unsupervised monocular depth estimation, and dedicated losses are meticulously designed to offer robust supervision for network training based on this novel integration. Noteworthy, we rely on the favorable property of the resulting albedo map of IID to circumvent the challenging images characteristics instead of pre-processing the input frames. The proposed method is extensively validated on SCARED and Hamlyn datasets, and better results are obtained than state-of-the-art techniques. Beside, its generalization ability and the effectiveness of the proposed components are also validated. This innovative method has the potential to elevate the quality of 3D reconstruction in monocular endoscopy, thereby enhancing the accuracy and robustness of augmented reality navigation technology in MIS. Our code will be available at: https://github.com/bobo909/IID-SfmLearner.