The current study focuses on systematically analyzing the recent advances in
the field of Multimodal eXplainable Artificial Intelligence (MXAI). In
particular, the relevant primary prediction tasks and publicly available
datasets are initially described. Subsequently, a structured presentation of
the MXAI methods of the literature is provided, taking into account the
following criteria: a) The number of the involved modalities, b) The stage at
which explanations are produced, and c) The type of the adopted methodology
(i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are
discussed. Finally, a comprehensive analysis of current challenges and future
research directions is provided.

本文系统分析了多模态可解释人工智能的最新进展，主要聚焦于相关的主要预测任务、公开可用的数据集、各类 MXAI 方法、评价指标以及未来研究方向和当前挑战。

多模态可解释人工智能：方法学进展及未来研究方向综述

Multimodal Explainable Artificial Intelligence: A Comprehensive Review  of Methodological Advances and Future Research Directions

Active visual exploration addresses the issue of limited sensor capabilities
in real-world scenarios, where successive observations are actively chosen
based on the environment. To tackle this problem, we introduce a new technique
called Attention-Map Entropy (AME). It leverages the internal uncertainty of
the transformer-based model to determine the most informative observations. In
contrast to existing solutions, it does not require additional loss components,
which simplifies the training. Through experiments, which also mimic
retina-like sensors, we show that such simplified training significantly
improves the performance of reconstruction, segmentation and classification on
publicly available datasets.

介绍了一种新技术 - 注意力地图熵（AME），该技术利用基于 Transformer 的模型的内部不确定性来确定最信息化的观测点，以改善现实场景中有限传感器能力的问题。通过使用网膜类传感器进行实验，研究表明，这种简化的训练在公开数据集上显着提高了重建，分割和分类的性能。

基于注意力地图熵的主动视觉探索

Active Visual Exploration Based on Attention-Map Entropy

Interest in dialog systems has grown substantially in the past decade. By
extension, so too has interest in developing and improving intent
classification and slot-filling models, which are two components that are
commonly used in task-oriented dialog systems. Moreover, good evaluation
benchmarks are important in helping to compare and analyze systems that
incorporate such models. Unfortunately, much of the literature in the field is
limited to analysis of relatively few benchmark datasets. In an effort to
promote more robust analyses of task-oriented dialog systems, we have conducted
a survey of publicly available datasets for the tasks of intent classification
and slot-filling. We catalog the important characteristics of each dataset, and
offer discussion on the applicability, strengths, and weaknesses of each. Our
goal is that this survey aids in increasing the accessibility of these
datasets, which we hope will enable their use in future evaluations of intent
classification and slot-filling models for task-oriented dialog systems.

该研究调查了公开可用的意图分类和填槽任务数据集，并对每个数据集的重要特征进行编目，以提高这些数据集的可访问性，便于未来评估对话系统的意图分类和填槽模型。

面向任务导向对话的意图分类和槽位填充数据集调查

A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog

The use of fundus images for the early screening of eye diseases is of great
clinical importance. Due to its powerful performance, deep learning is becoming
more and more popular in related applications, such as lesion segmentation,
biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it
is very necessary to summarize the recent developments in deep learning for
fundus images with a review paper. In this review, we introduce 143 application
papers with a carefully designed hierarchy. Moreover, 33 publicly available
datasets are presented. Summaries and analyses are provided for each task.
Finally, limitations common to all tasks are revealed and possible solutions
are given. We will also release and regularly update the state-of-the-art
results and newly-released datasets at this https URL Review
to adapt to the rapid development of this field.

该综述论文介绍了 143 篇深度学习在眼底图像方面的应用研究以及 33 个公开数据集，并对每个任务进行了总结和分析，并揭示了所有任务的共同局限性并提出了可能的解决方案。