arXiv subjects

Wentao Xiang

Publications and source records attributed to Wentao Xiang.

5 recordsLinked to original sources

arXiv2026

CogPic: A Multimodal Dataset for Early Cognitive Impairment Assessment via Picture Description Tasks

The automated evaluation of cognitive status using multimedia technologies offers a promising avenue for early dementia detection. However, the development of robust machine learning models for cognitive impairment detection is frequently hindered by the scarcity of large-scale, strictly synchronized, and clinically validated multimodal datasets. To bridge this critical gap, we introduce CogPic, a comprehensive Mandarin multimodal benchmark designed for fine-grained cognitive-status assessment. CogPic comprises strictly synchronized acoustic, visual, and linguistic data collected from 574 participants across three standardized picture-description tasks. To establish reliable diagnostic reference labels, expert clinical neuropsychologists conducted comprehensive evaluations and stratified participants into Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) groups through clinical consensus. Extensive benchmark experiments spanning handcrafted and deep learning models in unimodal and multimodal settings yield a best trimodal Macro-F1 of 58.34\%. To our knowledge, CogPic stands as the largest, most modality-rich, and most comprehensively characterized Mandarin dataset of its kind to date. Together, CogPic and its extensive baseline evaluations establish a rigorous empirical foundation for future multimedia research toward robust and clinically generalizable automated cognitive health assessment.

cs.DB↗

arXiv2026

MECO: A Multimodal Dataset for Emotion and Cognitive Understanding in Older Adults

While affective computing has advanced considerably, multimodal emotion prediction in aging populations remains underexplored, largely due to the scarcity of dedicated datasets. Existing multimodal benchmarks predominantly target young, cognitively healthy subjects, neglecting the influence of cognitive decline on emotional expression and physiological responses. To bridge this gap, we present MECO, a Multimodal dataset for Emotion and Cognitive understanding in Older adults. MECO includes 42 participants and provides approximately 38 hours of multimodal signals, yielding 30,592 synchronized samples. To maximize ecological validity, data collection followed standardized protocols within community-based settings. The modalities cover video, audio, electroencephalography (EEG), and electrocardiography (ECG). In addition, the dataset offers comprehensive annotations of emotional and cognitive states, including self-assessed valence, arousal, six basic emotions, and Mini-Mental State Examination cognitive scores. We further establish baseline benchmarks for both emotion and cognitive prediction. MECO serves as a foundational resource for multimodal modeling of affect and cognition in aging populations, facilitating downstream applications such as personalized emotion recognition and early detection of mild cognitive impairment (MCI) in real-world settings. The complete dataset and supplementary materials are available at https://maitrechen.github.io/meco-page/.

cs.HC↗

arXiv2025

Taiji Patterns Swirled Out of Magnetic Liquid Metal Fluids under Rotating Magnets

While magnetic fluids are well known for their rich ferrohydrodynamic behaviors, prior researches on dynamic droplet morphologies have largely been confined to the nonconducting matters. From an alternative, the electrically conductive magnetic liquid metals offer ever larger space for explorations meanwhile also incubate intriguing mysteries that had not been understood before. Here, we disclosed a group of rather profound fluidic phenomena happening on the magnetic liquid metals with sizes spanning from millimeter to centimeter scales when subject to the impact of a rotating magnet pair. We conceived that the identified flow patterns highly resemble that of the Chinese Taiji diagrams widely known as a classical theory to describe all things genesis and transformation, indicating the kaleidoscopic variations and intrinsic clues between modern magnetic liquid metal physics and the nature rules lying behind the ancient Eastern philosophy. Through systematically tuning the droplet volume, magnetic field strength, and magnet rotational speed, we achieved precise control over the liquid metal fluidic morphologies such as ellipses, dumbbells, toroidal rings, and Yin-Yang symbols, and quantified their dependence on the prescribed experimental conditions. Following the routes to construct the Taiji eight diagrams and their derivatives, we classified the disclosed flow patterns into eight representative schemes and three phase diagrams were plotted to characterize the fluidic patterns of the magnetic droplets correspondingly. The underlying mechanisms of magnetic liquid metal flow separation and coalescence were further interpreted from an energy perspective. These findings suggest enormous experimental insights and a theoretical framework for advancing fundamental magnetohydrodynamics and related engineering practices.

physics.flu-dyn↗

arXiv2025

Nav-$R^2$ Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation

Object-goal navigation in open-vocabulary settings requires agents to locate novel objects in unseen environments, yet existing approaches suffer from opaque decision-making processes and low success rate on locating unseen objects. To address these challenges, we propose Nav-$R^2$, a framework that explicitly models two critical types of relationships, target-environment modeling and environment-action planning, through structured Chain-of-Thought (CoT) reasoning coupled with a Similarity-Aware Memory. We construct a Nav$R^2$-CoT dataset that teaches the model to perceive the environment, focus on target-related objects in the surrounding context and finally make future action plans. Our SA-Mem preserves the most target-relevant and current observation-relevant features from both temporal and semantic perspectives by compressing video frames and fusing historical observations, while introducing no additional parameters. Compared to previous methods, Nav-R^2 achieves state-of-the-art performance in localizing unseen objects through a streamlined and efficient pipeline, avoiding overfitting to seen object categories while maintaining real-time inference at 2Hz. Resources will be made publicly available at \href{https://github.com/AMAP-EAI/Nav-R2}{github link}.

cs.CV↗

arXiv2025

Advancing Visual Large Language Model for Multi-granular Versatile Perception

Perception is a fundamental task in the field of computer vision, encompassing a diverse set of subtasks that can be systematically categorized into four distinct groups based on two dimensions: prediction type and instruction type. Notably, existing researches often focus solely on a limited subset of these potential combinations, which constrains their applicability and versatility across various contexts. In response to this challenge, we present MVP-LM, a Multi-granular and Versatile Perception framework incorporating Visual Large Language Model. Our framework is designed to integrate both word-based and sentence-based perception tasks alongside box and mask predictions within a single architecture. MVP-LM features an innovative multi-granularity decoder in conjunction with a CoT-inspired dataset unification strategy, enabling seamless supervised fine-tuning across a wide spectrum of tasks, including but not limited to panoptic segmentation, detection, grounding, and referring expression segmentation. Furthermore, we introduce a query enhancement strategy aimed at harnessing the decoding and generative capabilities inherent in VLLMs. Extensive experiments conducted across a range of benchmarks in both word-based and sentence-based perception tasks substantiate the efficacy of our framework. The code will be available at https://github.com/xiangwentao666/MVP-LM.

cs.CV↗