Generalizing WiFi Gesture Recognition via Large-Model-Aware Semantic Distillation and Alignment

• URL: http://arxiv.org/abs/2510.13390v1
• Date: Wed, 15 Oct 2025 10:28:50 GMT
• Title: Generalizing WiFi Gesture Recognition via Large-Model-Aware Semantic Distillation and Alignment
• Authors: Feng-Qi Cui, Yu-Tong Guo, Tianyue Zheng, Jinyang Huang,
• Abstract summary: WiFi-based gesture recognition has emerged as a promising RF sensing paradigm for AIoT environments.<n>We propose a novel generalization framework, termed Large-Model-Aware Semantic Distillation and Alignment.<n>Our method offers a scalable and deployable solution for generalized RF-based gesture interfaces in real-world AIoT applications.
• Score: 6.124050993047708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: WiFi-based gesture recognition has emerged as a promising RF sensing paradigm for enabling non-contact and privacy-preserving human-computer interaction in AIoT environments. However, existing methods often suffer from limited generalization and semantic expressiveness due to the domain-sensitive nature of Channel State Information and the lack of high-level gesture abstraction. To address these challenges, we propose a novel generalization framework, termed Large-Model-Aware Semantic Distillation and Alignment (GLSDA), which leverages the semantic prior of pre-trained large foundation models to enhance gesture representation learning in both in-domain and cross-domain scenarios. Specifically, we first design a dual-path CSI encoding pipeline that captures geometric and dynamic gesture patterns via CSI-Ratio phase sequences and Doppler spectrograms. These representations are then fed into a Multiscale Semantic Encoder, which learns robust temporal embeddings and aligns them with gesture semantics through cross-modal attention mechanisms. To further enhance category discrimination, we introduce a Semantic-Aware Soft Supervision scheme that encodes inter-class correlations and reduces label ambiguity, especially for semantically similar gestures. Finally, we develop a Robust Dual-Distillation strategy to compress the aligned model into a lightweight student network, jointly distilling intermediate features and semantic-informed soft labels from the teacher model. Extensive experiments on the Widar3.0 benchmark show that GLSDA consistently outperforms state-of-the-art methods in both in-domain and cross-domain gesture recognition tasks, while significantly reducing model size and inference latency. Our method offers a scalable and deployable solution for generalized RF-based gesture interfaces in real-world AIoT applications.

Related papers

• Semantic Waveforms for AI-Native 6G Networks [1.1224290226501585]
  We propose a semantic-aware waveform design framework for AI-native 6G networks.<n>Our approach enables controlled degradation of the wireless transmitted signal to preserve semantically significant content.<n> OSSDM outperforms conventional OFDM waveforms in spectral efficiency and semantic fidelity.
  arXiv  Detail & Related papers  (2026-02-10T12:55:37Z)
• Bidirectional Cross-Perception for Open-Vocabulary Semantic Segmentation in Remote Sensing Imagery [1.0742675209112622]
  Training-free open-vocabulary semantic segmentation (OVSS) methods typically fuse CLIP and vision foundation models (VFMs)<n>We propose a spatial-regularization-aware dual-branch collaborative inference framework for training-free OVSS, termed SDCI.<n> Experiments on multiple remote sensing semantic segmentation benchmarks demonstrate that our method achieves better performance than existing approaches.
  arXiv  Detail & Related papers  (2026-01-29T01:46:03Z)
• LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model [18.564067196226436]
  We propose a text-to-motion diffusion model that incorporates dual-path anchoring to enhance semantic alignment.<n>LUMA achieves state-of-the-art performance, with FID scores of 0.035 and 0.123, respectively.
  arXiv  Detail & Related papers  (2025-09-29T17:58:28Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• Wi-CBR: Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition [8.028748052177146]
  We propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR)<n>Specifically, we propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR)
  arXiv  Detail & Related papers  (2025-06-13T09:38:57Z)
• DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models [5.027492394254859]
  DiSa is a Directional Saliency-Aware Prompt Learning framework.<n>It integrates two complementary regularization strategies to enhance generalization.<n>It consistently outperforms state-of-the-art prompt learning methods across various settings.
  arXiv  Detail & Related papers  (2025-05-26T00:14:52Z)
• Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID [82.12123628480371]
  Unsupervised person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations for model learning.<n>Previous methods unify pseudo-labels of cross-modality images through label association algorithms and then design contrastive learning framework for global feature learning.<n>We propose a Semantic-Aligned Learning with Collaborative Refinement (SALCR) framework, which builds up objective for specific fine-grained patterns emphasized by each modality.
  arXiv  Detail & Related papers  (2025-04-27T13:58:12Z)
• Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-view Geo-Localization [20.603433987118837]
  Drone-view Geo-Localization (DVGL) aims to achieve accurate localization of drones by retrieving the most relevant GPS-tagged satellite images.<n>Existing methods heavily rely on strictly pre-paired drone-satellite images for supervised learning.<n>We propose a novel end-to-end self-supervised learning method with a shallow backbone network.
  arXiv  Detail & Related papers  (2025-02-17T02:53:08Z)
• MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition [94.56755080185732]
  We propose a Motion-Aware masked autoencoder with Semantic Alignment (MASA) that integrates rich motion cues and global semantic information. Our framework can simultaneously learn local motion cues and global semantic features for comprehensive sign language representation.
  arXiv  Detail & Related papers  (2024-05-31T08:06:05Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition [63.07844685982738]
  This paper presents a new model named as Gated Bidirectional Alignment Network (GBAN), which consists of an attention-based bidirectional alignment network over LSTM hidden states. We empirically show that the attention-aligned representations outperform the last-hidden-states of LSTM significantly. The proposed GBAN model outperforms existing state-of-the-art multimodal approaches on the IEMOCAP dataset.
  arXiv  Detail & Related papers  (2022-01-17T09:46:59Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.