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- MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing203
MM-Fi is proposed, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks.
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- IoT-LLM: A framework for enhancing large language model reasoning from real-world sensor data55
IoT-LLM is proposed, a unified framework that significantly improves the performance of IoT-sensory task reasoning of LLMs, with models such as GPT-4o-mini showing a 49.4% average improvement over previous methods.
- MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation45
A WiFi-based IoT-enabled human pose estimation scheme for metaverse avatar simulation, namely MetaFi is proposed, where a deep neural network is designed with customized convolutional layers and residual blocks to map the channel state information to human pose landmarks.
- TENT: Connect Language Models With IoT Sensors for Zero-Shot Activity Recognition41
TENT not only achieves robust recognition of both seen and unseen activities but also significantly outperforms existing vision–language and sensor-language baselines, surpassing them by over 20% on zero-shot HAR tasks, establishing TENT as a new paradigm for generalizable IoT representation learning.
- AdaPose: Toward Cross-Site Device-Free Human Pose Estimation With Commodity WiFi32
A domain adaptation algorithm, AdaPose, designed specifically for WiFi-based pose estimation that aims to identify consistent human poses that are highly resistant to environmental dynamics and WiFi signal noises and facilitates the widespread application of WiFi-based pose estimation in smart cities.
- PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station15
A novel channel state information (CSI)-based pose estimation framework, namely, PowerSkel, is developed to address safety monitoring of power operations in power stations and significantly reduces the deployment cost and complexity compared to the existing solutions.
- M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction11
This work introduces M4Human, the current largest-scale (661K-frame) ($9\times$ prior largest) multimodal benchmark, featuring high-resolution mmWave radar, RGB, and depth data, and establishes benchmarks on both RT and RPC modalities, as well as multimodal fusion with RGB-D modalities.
- Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation10
A novel SSA2lign is proposed to address FSVDA at the snippet level, where the target domain is expanded through a simple snippet-level augmentation followed by the attentive alignment of snippets both semantically and statistically, where semantic alignment of snippets is conducted through multiple perspectives.
- T3DNet: Compressing Point Cloud Models for Lightweight 3-D Recognition5
This article predefine a tiny model and improves its performance through auxiliary supervision from augmented networks and the original model, achieving state-of-the-art performances on three datasets against existing methods.
- SkeFi: Cross-Modal Knowledge Transfer for Wireless Skeleton-Based Action Recognition2
This work proposes the enhanced temporal correlation adaptive graph convolution (TC-AGC) with frame interactive enhancement to overcome the noise from missing or noncontinuous frames and underscores the effectiveness of enhancing multiscale temporal modeling (MST) through dual temporal convolution.
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- A New FE Modelling Approach to Spot Welding Joints of Automotive Panels and Modal Characteristics1
Based on the orthogonal experiments, the effect of the parameters of spot-welding structure, such as overlap proportion, spot pitch, etc., on the modes of the connected panels has been emphatically investigated, and its law is obtained.
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- Research on video image stabilization decision algorithm based on ROI block matching method–
The experimental results show that the proposed algorithm can effectively reduce the video stabilization in collision experiments in the subjective evaluation and the objective evaluation of local peak signal-to-noise ratio.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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