The paper extends the Cooperative Multi-Task Semantic Communication (CMT‑SemCom) framework to jointly perform heterogeneous classification and regression tasks on the Cityscapes dataset, using an InfoMax principle to handle mixed discrete and continuous semantic variables. It compares the new framework against independent single‑task training, conventional task‑agnostic digital transmission, and single‑encoder multi‑decoder SemCom, and studies how the capacity of the common unit affects joint task performance. Extensive evaluations show that CMT‑SemCom outperforms all benchmarks.
By Ahmad Halimi Razlighi, Mohammad Siddiqur Rahman, Maximilian H. V. Tillmann, Edgar Beck, Armin Dekorsy
The paper extends the Cooperative Multi-Task Semantic Communication (CMT‑SemCom) framework to handle heterogeneous classification and regression tasks on the Cityscapes dataset. By incorporating the InfoMax principle, the system accommodates mixed discrete and continuous semantic variables, and it is benchmarked against single‑task training, task‑agnostic digital transmission, and single‑encoder multi‑decoder SemCom. Experiments show that CMT‑SemCom outperforms all baselines and provide insights into how the common unit capacity affects joint task performance.
The paper proposes a task-oriented semantic feature transmission framework for satellite remote sensing over low‑signal‑to‑noise ratio (SNR) channels. Instead of reconstructing images first, it directly transmits semantic features extracted by a multitask‑pretrained backbone, using a lightweight channel adaptation module to reduce bandwidth and a feature restorer to recover task‑relevant structure after channel corruption. Experiments on scene classification and object detection under additive white Gaussian noise show consistent improvements over reconstruction‑oriented joint source‑channel coding baselines, especially in the low‑SNR regime.
By Shuoyuan Sun, Hongyu Wang, Mugen Peng, Wenjia Xu
ShallowStream is a framework for streaming video understanding that uses the shallow layers of a multimodal large language model (MLLM) to encode frames and build a lightweight index. During streaming, it maintains an always‑on index via the KV cache of shallow layers, and at query time it scores context frames using shallow‑layer attention and selects diverse evidence for answering. The approach matches the performance of leading streaming methods while cutting per‑frame prefill latency and 10‑second end‑to‑end latency by up to 52.1× and 11.9×, respectively.
By Jitai Hao, Ke Yang, Qiang Huang, Jun Yu
The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.
By Jiwoong Im, Minwoo Kim, Jaeho Lee, Yo-Seb Jeon, Yongjune Kim
arXiv:2606. 10277v1 Announce Type: new Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstream tasks remains constrained by existing paradigms.
By Yuxuan Shi, Tingting Yang, Kangning Ma, Liwen Jing, Yuwei Wang, Mengfan Zheng, Li Sun
arXiv:2609.35490v2 Announce Type: replace
Abstract: Generalist multitasking vision models aim to unify multiple vision tasks within a single framework, enabling more efficient and versatile learning....
By Mohammad Mahdi, Nedyalko Prisadnikov, Yuqian Fu, Carmelo Scribano, Danda Pani Paudel, Luc Van Gool
arXiv:2607. 13494v1 Announce Type: new Abstract: The development of smart transportation systems and the introduction of 6G wireless communication technologies have significantly changed vehicle network topologies.
By S. M. Abtahiul Alam, Niloy Das, Apurba Adhikary, Yu Qiao, Zhu Han, Choong Seon Hong
arXiv:2608. 00056v1 Announce Type: cross Abstract: We propose RSTA, a domain-generalized semantic communication framework enabling source-free V2X collaborative perception under both observation-domain shift and unseen wireless channel conditions.
By Fan Gao, Youzheng Wang, Ning Ge
arXiv:2609.39296v1 Announce Type: new
Abstract: Video semantic communication has attracted increasing attention as a promising approach to improving video transmission efficiency. However, most exist...
By Xiangben Zhu, Caili Guo, Yang Yang, Chuanhong Liu, Meiyi Zhu
The paper introduces a semantic‑aware multi‑level neural video codec designed for low‑latency, task‑oriented video transmission over unreliable channels. It builds on the real‑time DCVC‑RT codec by partitioning encoded representations into packets of varying semantic and feature importance, assigning them to priority streams, and employing an error‑resilient entropy model that removes inter‑packet dependencies. Experiments demonstrate that this framework improves robustness against packet erasures, achieving graceful degradation in less important regions while preserving task‑relevant visual content.
By Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du, Harish Viswanathan
arXiv:2607. 16877v1 Announce Type: cross Abstract: The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications.
By Yangjing Wang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li