Showing papers in "Information Fusion in 2022"

TL;DR: This study surveyed the current progress of XAI and in particular its advances in healthcare applications, and introduced the solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios.

TL;DR: In this paper , the authors explore whether deep learning models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets.

TL;DR: Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made as mentioned in this paper , which is particularly true of the most popular deep neural network approaches currently in use.

TL;DR: In this article , a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.

TL;DR: Tang et al. as discussed by the authors proposed a semantic-aware real-time image fusion network (SeAFusion), which cascaded the image fusion module and semantic segmentation module and leveraged the semantic loss to guide high-level semantic information to flow back to the fusion module.

TL;DR: In this paper, a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.

TL;DR: Tang et al. as mentioned in this paper proposed a progressive IR/VIS fusion method based on illumination aware, which adaptively maintains the intensity distribution of salient targets and preserves texture information in the background.

TL;DR: Zhang et al. as mentioned in this paper proposed a brain tumor segmentation method based on the fusion of deep semantics and edge information in multimodal MRI, aiming to achieve a more sufficient utilization of multi-modal information for accurate segmentation.

TL;DR: Li et al. as discussed by the authors made a comprehensive review on real-world single image super-resolution (RSISR), and four major categories of RSISR methods, namely the degradation modeling-based, image pairsbased, domain translation-based and self-learning-based SR methods.

TL;DR: Li et al. as discussed by the authors made a comprehensive review on real-world single image super-resolution (RSISR), and four major categories of RSISR methods, namely the degradation modeling-based, image pairsbased, domain translation-based and self-learning-based SR methods.

TL;DR: A novel personalized diagnosis technique is proposed for early Alzheimer’s disease diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture and outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis.

TL;DR: This systematic overview of affective computing systematically review recent advances, survey and taxonomize state-of-the-art unimodal affects recognition and multimodal affective analysis in terms of their detailed architectures and performances, and concludes with an indication of the most promising future directions.

TL;DR: In this paper , the authors provide a brief overview of the resource allocation methods for fused target tracking in radar sensor network and divide them into two types, namely tracking quality constrained RA type and performance driven RA type.

TL;DR: In this paper , the authors describe three frontier research areas facilitating ethical responsible and legally compliant medical AI: complex networks and their inference, graph causal models and counterfactuals, and explainability methods.

TL;DR: In this article, the authors describe three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods.

TL;DR: A comprehensive review of state-of-the-art multimodal affect recognition and affective analysis can be found in this article , where the authors introduce two typical emotion models followed by five kinds of commonly used databases for affective computing.

TL;DR: In this paper , a systematic review of computational data harmonization approaches for multi-modality data in the digital healthcare field, including harmonization strategies and evaluation metrics based on different theories, is presented.

TL;DR: A novel contrastive learning scheme by including the labels in the same embedding space as the features and performing the distance comparison between features and labels in this sharedembedding space, which drastically reduces the number of pair-wise comparisons, thus improving model performance.

TL;DR: This article performed a Latent Dirichlet topic modeling analysis (LDA) under a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to find the most relevant literature articles.

TL;DR: A detailed overview of the latest trends in research pertaining to visual and language modalities is presented, looking at its applications in their task formulations and how to solve various problems related to semantic perception and content generation.

TL;DR: A comprehensive review of exploration techniques in deep reinforcement learning can be found in this article , where the authors provide a comprehensive overview of existing exploration approaches, which are categorised based on the key contributions as: reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based method, safe exploration and random-based techniques.

TL;DR: In this paper , a novel personalized diagnosis technique is proposed for early Alzheimer's disease (AD) diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture, where the module of interpretable learning selects informative features to provide interpretability for disease diagnosis and abandons redundant features to capture inherent correlation of data points.

TL;DR: A Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, which outperforms state-of-the-art panshARPening methods.

TL;DR: In this article, a review of the DL methods for automatic detection of depression to extract a representation of depression from audio and video is presented. And the challenges and promising directions related to the automatic diagnoses of depression using DL are discussed.

TL;DR: A termination index is developed to terminate the consensus reaching process (CRP) to make the CRP more objective and rational and to validate the feasibility and effectiveness of the proposed model.

TL;DR: In this article , the authors used deep learning (DL) to extract a representation of depression cues from audio and video for automatic depression detection. But, they did not discuss challenges and promising directions related to the automatic diagnoses of depression using DL.

TL;DR: In this article , the authors present an extensive review of the threats of federated learning, as well as their corresponding countermeasures, attacks versus defences, and expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack.

TL;DR: Zhang et al. as discussed by the authors reviewed the specific research status of deep learning technology in the field of image inpainting in the past 15 years; then, they deeply studied and analyzed the existing image restoration methods based on different neural network structures and their information fusion methods.

TL;DR: The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.

TL;DR: A novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality3D reconstruction, including a data fitting term and a robust low-rank matrix factorization constraint is investigated.