Tootfinder

Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems
Junjie Ni, Tong Wu, Zhiyong Chen, Yin Xu, Meixia Tao, Wenjun Zhang
https://arxiv.org/abs/2509.09499

Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems
In this paper, we propose a mixture of semantics (MoS) transmission strategy for wireless semantic communication systems based on generative artificial intelligence (AI). At the transmitter, we divide an image into regions of interest (ROI) and reigons of non-interest (RONI) to extract their semantic information respectively. Semantic information of ROI can be allocated more bandwidth, while RONI can be represented in a compact form for transmission. At the receiver, a diffusion model reconstru…

"Ist Avocado #Obst oder #Gemüse? Was #Avocados uns über Semantik lehren" @ [di.t͡svi.blΩ]:

Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
Dubing Chen, Huan Zheng, Yucheng Zhou, Xianfei Li, Wenlong Liao, Tao He, Pai Peng, Jianbing Shen
https://arxiv.org/abs/2509.08388

Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
Vision-based 3D semantic occupancy prediction is a critical task in 3D vision that integrates volumetric 3D reconstruction with semantic understanding. Existing methods, however, often rely on modular pipelines. These modules are typically optimized independently or use pre-configured inputs, leading to cascading errors. In this paper, we address this limitation by designing a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline. Ground…

Privacy Preserving Semantic Communications Using Vision Language Models: A Segmentation and Generation Approach
Haoran Chang, Mingzhe Chen, Huaxia Wang, Qianqian Zhang
https://arxiv.org/abs/2509.08142 …

Privacy Preserving Semantic Communications Using Vision Language Models: A Segmentation and Generation Approach
Semantic communication has emerged as a promising paradigm for next-generation wireless systems, improving the communication efficiency by transmitting high-level semantic features. However, reliance on unimodal representations can degrade reconstruction under poor channel conditions, and privacy concerns of the semantic information attack also gain increasing attention. In this work, a privacy-preserving semantic communication framework is proposed to protect sensitive content of the image dat…

OTESGN:Optimal Transport Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis
Xinfeng Liao, Xuanqi Chen, Lianxi Wang, Jiahuan Yang, Zhuowei Chen, Ziying Rong
https://arxiv.org/abs/2509.08612

OTESGN:Optimal Transport Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to identify aspect terms and determine their sentiment polarity. While dependency trees combined with contextual semantics effectively identify aspect sentiment, existing methods relying on syntax trees and aspect-aware attention struggle to model complex semantic relationships. Their dependence on linear dot-product features fails to capture nonlinear associations, allowing noisy similarity from irrelevant words to obscure key opinion terms. Motivate…

Generalized User-Oriented Image Semantic Coding Empowered by Large Vision-Language Model
Sin-Yu Huang, Vincent W. S. Wong
https://arxiv.org/abs/2509.08913 https://

Generalized User-Oriented Image Semantic Coding Empowered by Large Vision-Language Model
Semantic communication has shown outstanding performance in preserving the overall source information in wireless transmission. For semantically rich content such as images, human users are often interested in specific regions depending on their intent. Moreover, recent semantic coding models are mostly trained on specific datasets. However, real-world applications may involve images out of the distribution of training dataset, which makes generalization a crucial but largely unexplored problem…

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Peigen Ye, Jingpu Duan, Hongyang Du, Yulan Guo
https://arxiv.org/abs/2508.05884 https://

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention pr…

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation
Wenjun Yu, Yinchen Zhou, Jia-Xuan Jiang, Shubin Zeng, Yuee Li, Zhong Wang
https://arxiv.org/abs/2509.08570

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation
Multimodal models have achieved remarkable success in natural image segmentation, yet they often underperform when applied to the medical domain. Through extensive study, we attribute this performance gap to the challenges of multimodal fusion, primarily the significant semantic gap between abstract textual prompts and fine-grained medical visual features, as well as the resulting feature dispersion. To address these issues, we revisit the problem from the perspective of semantic aggregation. S…

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs
Vadim Zadykian, Bruno Andrade, Haithem Afli
https://arxiv.org/abs/2509.09522

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs
Semantic Textual Relatedness (STR) captures nuanced relationships between texts that extend beyond superficial lexical similarity. In this study, we investigate STR in the context of job title matching - a key challenge in resume recommendation systems, where overlapping terms are often limited or misleading. We introduce a self-supervised hybrid architecture that combines dense sentence embeddings with domain-specific Knowledge Graphs (KGs) to improve both semantic alignment and explainability…

EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs
Yuhao Zhang, Yuhao Du, Zhanchen Dai, Xiangnan Ma, Kaiqi Kou, Benyou Wang, Haizhou Li
https://arxiv.org/abs/2509.09174

EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs
Speech-to-speech large language models (SLLMs) are attracting increasing attention. Derived from text-based large language models (LLMs), SLLMs often exhibit degradation in knowledge and reasoning capabilities. We hypothesize that this limitation arises because current training paradigms for SLLMs fail to bridge the acoustic-semantic gap in the feature representation space. To address this issue, we propose EchoX, which leverages semantic representations and dynamically generates speech trainin…