Deception detection has garnered increasing attention in recent years due to the significant growth of digital media and heightened ethical and security concerns. It has been extensively studied using multimodal methods, including video, audio, and text. In addition, individual differences in deception production and detection are believed to play a crucial role. Although some studies have utilized individual information such as personality traits to enhance the performance of deception detection, current systems remain limited, partly due to a lack of sufficient datasets for evaluating performance. To address this issue, we introduce a multimodal deception dataset MDPE. Besides deception features, this dataset also includes individual differences information in personality and emotional expression characteristics. It can explore the impact of individual differences on deception behavior. It comprises over 104 hours of deception and emotional videos from 193 subjects. Furthermore, we conducted numerous experiments to provide valuable insights for future deception detection research. MDPE not only supports deception detection, but also provides conditions for tasks such as personality recognition and emotion recognition, and can even study the relationships between them. We believe that MDPE will become a valuable resource for promoting research in the field of affective computing.

The fusion of hyperspectral image (HSI) and multispectral image (MSI) is an effective mean to improve the inherent defect of low spatial resolution of HSI. However, existing fusion methods usually rigidly upgrade the spatial resolution of HSI to that of matching MSI under the ideal assumption that multi-source images are accurately registered. In real scenes where multi-source images are difficult to be perfectly registered and the spatial resolution requirements are dynamically different, these fusion algorithms is difficult to be effectively deployed. To this end, we construct the spatial-spectral consistent arbitrary scale observation model (S2cAsOM) to model the dependence between the unregistered HSI and MSI and the ideal arbitrary resolution HSI. Furthermore, an optimization algorithm is designed to solve S2cAsOM, and a deep interpretable arbitrary resolution fusion network (IR&ArF) is proposed to simulate the optimization process, which achieves the model-data dual-driven arbitrary resolution fusion of unregistered HSI and MSI. IR&ArF breaks the dependence of traditional fusion methods on the accuracy of image registration in a robust way, and can flexibly cope with the dynamic requirements of diverse applications for the spatial resolution of HSI, which improves the application ability of HSI fusion in real scenes. Extensive systematic experiments demonstrate the superiority and generalization of the proposed method. Source code of the proposed method is available on https://github.com/Jiahuiqu/IR-ArF.

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.

Hyperspectral image super-resolution (HSISR) has shown very promising potential for earth observation and deep space exploration tasks. However, most existing HSISR methods formulate HSISR tasks with different scale factors as independent tasks, and train a specific model for each scale factor. In this letter, we propose a coarse-to-fine meta diffusion HSISR method, termed as CFMDM, which is capable of solving the problem of HSISR with scale-arbitrary factors in a unified model. The proposed CFMDM is composed of a coarse-to-fine upsampling module. The module encompasses two pivotal units: a coarse meta upsampling unit that utilizes meta-learning to map features of arbitrary scales to the corresponding scales, and a gradual refinement diffusion unit, which is designed to refine the details of the reconstructed HSI. In addition, we develop an imaging model-driven downsampling algorithm for generating training samples tailored to practical applications. The proposed method performs well in both quantitative and qualitative evaluation on benchmark datasets, achieving the average PSNR of 41.45dB at 1.5x super-resolution for the CAVE dataset.