This survey reviews state‑of‑the‑art research up to Q1 2026 on integrating tactile sensing with vision and language for embodied AI, presenting a four‑stage fusion pipeline, a hierarchical taxonomy of datasets, methods, sensors, and highlighting current evaluation challenges and future directions.
Vision‑language models turn continuous images into discrete tokens through patch extraction, encoding, and projection, enabling Transformers to reason jointly over vision and text, but this compression introduces limits in spatial reasoning, counting, and resolution sensitivity that users must understand.
The article analyses AI‑augmented reality intent‑recognition systems, detailing multi‑modal data fusion, three‑way interaction architectures, and adaptive response mechanisms, and demonstrates their impact across medical surgery, elderly care, accessibility, and product design while outlining technical challenges and design methodologies.
This article examines how a major internet platform leverages large‑model AI—through multimodal fusion, script‑recognition fine‑tuning, and an automatic model‑iteration framework—to overcome precision, adversarial, and cross‑scenario challenges in fraud detection, and how agent‑based tools further boost operational efficiency.
This article examines the cutting‑edge AI techniques—multimodal fusion, deep‑learning disease detection, reversible repair, diffusion‑Transformer models, GAN‑based pattern generation, and AR navigation—that enable millimetre‑level digital restoration and cultural democratization of the Dunhuang murals.
This article summarizes four recent arXiv papers on time‑series forecasting, covering a hierarchical knowledge‑distillation framework for extreme events, a graph‑enhanced multimodal fusion network, an interpretable unsupervised anomaly detector, and an adaptive masking loss that improves prediction accuracy.
The article surveys 117 recent multimodal‑fusion papers, classifies them into improvement‑based and combination‑based approaches, highlights representative works such as TimeXL, OGP‑Net, MMR‑Mamba and FusionSight, and provides a free collection of papers, classic models and code repositories for researchers.
This article details Xiaohongshu's technical approaches to solving decentralized content distribution by enhancing side‑information usage, integrating multimodal signals across the recommendation pipeline, applying graph‑based models, and implementing interest exploration and protection mechanisms, while also outlining future research directions.
This article introduces M2Doc, a plug‑in multimodal fusion approach that equips visual‑only object detectors with textual and semantic awareness, detailing its early‑ and late‑fusion modules, experimental validation on DocLayNet, M6Doc and PubLayNet, and future research directions.
A team of university students built an AI‑powered eye‑disease diagnostic system using federated learning and cloud services, achieving 97.48% accuracy and winning a national award, while outlining future plans for 3D visualization and immersive medical interaction.
IEEE has officially released three new international standards—IEEE 2884-2023 for face recognition, IEEE 2891-2023 for fingerprint recognition, and IEEE 2859-2023 for multimodal fusion—developed by Ant Group and partners to provide unified, secure and cost‑effective testing frameworks for biometric systems worldwide.
The authors introduce a reinforcement‑learning‑driven memory network that augments long‑range dependencies for continuous valence‑arousal emotion prediction in movies, integrating five multimodal features and a DDPG‑based update policy, which yields state‑of‑the‑art performance across multiple affective‑analysis and summarization benchmarks.
This presentation explores how deep semantic role labeling and multimodal data fusion of biomedical literature can build fine‑grained knowledge graphs that enable drug repurposing, describing the AGAC corpus, modeling approaches, evaluation results, and future research directions.
The paper presents DeepDualMapper, a gated‑fusion deep network that combines aerial imagery and vehicle trajectory data to automatically generate high‑precision maps, detailing its architecture, gated and refinement modules, and experimental validation on three city datasets.
