Human pose estimation is a critical task in computer vision for applications in sports analysis, healthcare monitoring, and human-computer interaction. However, existing human pose datasets are collected either from custom-configured laboratories with complex devices or they only include data on single individuals, and both types typically capture daily activities. In this paper, we introduce the M3GYM dataset, a large-scale multimodal, multi-view, and multi-person pose dataset collected from a real gym to address the limitations of existing datasets. Specifically, we collect videos for 82 sessions from the gym, each session lasting between 40 to 60 minutes. These videos are gathered by 8 cameras, including over 50 subjects and 47 million frames. These sessions include 51 Normal fitness exercise sessions as well as 17 Pilates and 14 Yoga sessions. The exercises cover a wide range of poses and typical fitness activities, particularly in Yoga and Pilates, featuring poses with stretches, bends, and twists, e.g., humble warrior, fire hydrants and knee hover side twists. Each session involves multiple subjects, leading to significant self-occlusion and mutual occlusion in single views. Moreover, the gym has two symmetric floor mirrors, a feature not seen in previous datasets, and seven lighting conditions. We provide frame-level multimodal annotations, including 2D&3D keypoints, subject IDs, and meshes. Additionally, M3GYM uniquely offers labels for over 500 actions along with corresponding assessments from sports experts. We benchmark a variety of state-of-the-art methods for several tasks, i.e., 2D human pose estimation, single-view and multi-view 3D human pose estimation, and human mesh recovery. To simulate real-world applications, we also conduct cross-domain experiments across Normal, Yoga, and Pilates sessions. The results show that M3GYM significantly improves model generalization in complex real-world settings. The project is available here.

Misinformation is a significant societal issue with potentially severe consequences. It appears in text, image, audio, and video modalities, encompassing various categories such as unimodal deception (fact-conflicting, AI-generated & offensive content) and cross-modal inconsistencies. However, current detection approaches often focus on text and image, overlooking the growing prevalence of misinformation in audio and video content. Moreover, these methods typically tend to address only one or two types of misinformation, failing to address all categories simultaneously. These detectors are also usually designed to make judgments without providing explanations, reducing transparency and limiting their broader applicability. To address these issues, we propose MDAM3, a Misinformation Detection and Analysis Framework for Multitype Multimodal Media. MDAM3 analyzes each input in internal detection and examines relationships across modalities to identify inconsistencies. It utilizes web resources and integrates Large Vision-Language Models (LVLMs) to deliver accurate detection results along with detailed analysis. To evaluate MDAM3, we curate MDAM3-DB, a specialized multitype multimodal misinformation dataset. A user study is conducted to explore MDAM3's usability, interpretability, and effectiveness. We hope this research contributes to advancing misinformation detection methodologies and provides valuable insights for developing robust multimodal analysis tools.

Isolated Sign Language Recognition (ISLR) focuses on identifying individual sign language glosses. Considering the diversity of sign languages across geographical regions, developing region-specific ISLR datasets is crucial for supporting communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale word-level dataset for the ISLR task. To fill this gap, we curate \underline{\textbf{the first}} large-scale Multi-view Multi-modal Word-Level Australian Sign Language recognition dataset, dubbed MM-WLAuslan. Compared to other publicly available datasets, MM-WLAuslan exhibits three significant advantages: (1) the largest amount of data, (2) the most extensive vocabulary, and (3) the most diverse of multi-modal camera views. Specifically, we record 282K+ sign videos covering 3,215 commonly used Auslan glosses presented by 73 signers in a studio environment.Moreover, our filming system includes two different types of cameras, i.e., three Kinect-V2 cameras and a RealSense camera. We position cameras hemispherically around the front half of the model and simultaneously record videos using all four cameras. Furthermore, we benchmark results with state-of-the-art methods for various multi-modal ISLR settings on MM-WLAuslan, including multi-view, cross-camera, and cross-view. Experiment results indicate that MM-WLAuslan is a challenging ISLR dataset, and we hope this dataset will contribute to the development of Auslan and the advancement of sign languages worldwide. All datasets and benchmarks are available at MM-WLAuslan.

With the development of various generative models, misinformation in news media becomes more deceptive and easier to create, posing a significant problem. However, existing datasets for misinformation study often have limited modalities, constrained sources, and a narrow range of topics. These limitations make it difficult to train models that can effectively combat real-world misinformation. To address this, we propose a comprehensive, large-scale Multimodal Misinformation dataset for Media Authenticity Analysis ( M 3 A ), featuring broad sources and fine-grained annotations for topics and sentiments. To curate M 3 A , we collect genuine news content from 60 renowned news outlets worldwide and generate fake samples using multiple techniques. These include altering named entities in texts, swapping modalities between samples, creating new modalities, and misrepresenting movie content as news. M 3 A contains 708K genuine news samples and over 6M fake news samples, spanning text, images, audio, and video. M 3 A provides detailed multi-class labels, crucial for various misinformation detection tasks, including out-of-context detection and deepfake detection. For each task, we offer extensive benchmarks using state-of-the-art models, aiming to enhance the development of robust misinformation detection systems. • We present M 3 A , a large-scale multimodal misinformation dataset with diverse news samples. • M 3 A includes texts, images, audio, and videos from multiple reputable news outlets. • M 3 A addresses limitations in existing datasets in misinformation generation methods and scale. • We provide multi-class annotations in M 3 A for various key tasks in misinformation detection. • We propose benchmarks for M 3 A using state-of-the-art models and out-of-distribution testing.

In this article, the optimal tracking control problem for a class of discrete time-delay systems with disturbance is studied using the information fusion control methodology. First, the disturbance estimation is regarded as a minimization problem, and an information fusion disturbance observer (IFDO) is designed. Second, combining the soft constraint requirement of the output tracking error contained in the performance index function, a fusion filter is constructed to estimate the system co-state and its information weight under system state and input delays. Finally, the obtained co-state and its information weight, the soft constraint requirement of the control input contained in the performance function, as well as the system delay dynamics, are all transformed into the information equations about the control input, and then the information fusion delay controller (IFDC) is directly obtained by fusing these information equations. As a result, the negative effects caused by disturbance and time delays can be effectively compensated at the same time. The convergence of the tracking errors of the closed-loop system is guaranteed by the Lyapunov stability theory. Numerical simulation results have been provided to demonstrate the effectiveness and robustness of the proposed control algorithm.

The artificial endocrine system (AES) is a new branch of natural computing which uses ideas and takes inspiration from the information processing mechanisms contained in the mammalian endocrine system. It is a fast growing research field in which a variety of new theoretical models and technical methods have been studied for dealing with complex and significant problems. An overview of some recent advances in AES modeling and its applications is provided in this paper, based on the major and latest works. This review covers theoretical modeling, combinations of algorithms, and typical application fields. A number of challenges that can be undertaken to help move the field forward are discussed according to the current state of the AES approach.