8 citations · 38 across the 28 of their papers we have counts for
30 papers
Tokenized Joint Source-Network Coding for Loss-Resilient Video Streaming
Ke Yang, Sixian Wang, Kailin Tan +4
For decades, conventional video streaming systems have relied on packet-level forward error correction (FEC) codes and retransmissions to ensure reliable video delivery. However, i…
Loss-Resilient Semantic Communication over Packet-Loss Networks at Extreme-Low Bandwidth
Shengshi Yao, Jincheng Dai, Sixian Wang +5
In extreme-low bandwidth network scenarios, generative semantic codecs have emerged as promising solutions to reduce bandwidth cost for visual communications. However, these learne…
GVC-RT: Towards Real-Time Generative Video Compression at Ultra-Low Bitrates
Tianjian Dang, Sixian Wang, Lei Luo +2
Recent generative video codecs (GVCs) have achieved impressive reconstruction fidelity at ultra-low bitrates (< 0.02 bits per pixel) by compressing the tokens from generative token…
DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations
Kailin Tan, Jincheng Dai, Sixian Wang +5
Generative joint source-channel coding (GJSCC) has emerged as a new Deep JSCC paradigm for achieving high-fidelity and robust image transmission under extreme wireless channel cond…
Structure-Guided Allocation of 2D Gaussians for Image Representation and Compression
Huanxiong Liang, Yunuo Chen, Yicheng Pan +4
Recent advances in 2D Gaussian Splatting (2DGS) have demonstrated its potential as a compact image representation with millisecond-level decoding. However, existing 2DGS-based pipe…
Neural Hamiltonian Deformation Fields for Dynamic Scene Rendering
Hai-Long Qin, Sixian Wang, Guo Lu +1
Representing and rendering dynamic scenes with complex motions remains challenging in computer vision and graphics. Recent dynamic view synthesis methods achieve high-quality rende…
Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications
Hai-Long Qin, Jincheng Dai, Guo Lu +6
Semantic communications mark a paradigm shift from bit-accurate transmission toward meaning-centric communication, essential as wireless systems approach theoretical capacity limit…
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering
Sixian Wang, Zhiwei Tang, Tsung-Hui Chang
Diffusion models often exhibit inconsistent sample quality due to stochastic variations inherent in their sampling trajectories. Although training-based fine-tuning (e.g. DDPO [1])…
Neural Coding Is Not Always Semantic: Toward the Standardized Coding Workflow in Semantic Communications
Hai-Long Qin, Jincheng Dai, Sixian Wang +5
Semantic communication, leveraging advanced deep learning techniques, emerges as a new paradigm that meets the requirements of next-generation wireless networks. However, current s…
ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling
Sixian Wang, Jincheng Dai, Xiaoqi Qin +3
Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs te…
NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission
Weijie Yue, Zhongwei Si, Bolin Wu +5
We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decom…
SoundSpring: Loss-Resilient Audio Transceiver with Dual-Functional Masked Language Modeling
Shengshi Yao, Jincheng Dai, Xiaoqi Qin +4
In this paper, we propose "SoundSpring", a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also bein…
Rate-Distortion-Perception Controllable Joint Source-Channel Coding for High-Fidelity Generative Communications
Kailin Tan, Jincheng Dai, Zhenyu Liu +5
End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, ex…
Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency
Jincheng Dai, Xiaoqi Qin, Sixian Wang +3
Information theory and machine learning are inextricably linked and have even been referred to as "two sides of the same coin". One particularly elegant connection is the essential…
DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling
Sixian Wang, Jincheng Dai, Kailin Tan +3
End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical cond…
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
Ke Yang, Sixian Wang, Jincheng Dai +3
As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A g…
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials
Sixian Wang, Jincheng Dai, Xiaoqi Qin +2
Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neur…
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
Sixian Wang, Jincheng Dai, Xiaoqi Qin +3
Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel c…
Variational Speech Waveform Compression to Catalyze Semantic Communications
Shengshi Yao, Zixuan Xiao, Sixian Wang +3
We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear transform and variational modeling, we effective…
Wireless Deep Speech Semantic Transmission
Zixuan Xiao, Shengshi Yao, Jincheng Dai +3
In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over wireless channels. We name the whole system as…
WITT: A Wireless Image Transmission Transformer for Semantic Communications
Ke Yang, Sixian Wang, Jincheng Dai +3
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Prev…
Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding
Jincheng Dai, Sixian Wang, Ke Yang +5
The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned…
Versatile Semantic Coded Transmission over MIMO Fading Channels
Shengshi Yao, Sixian Wang, Jincheng Dai +2
Semantic communications have shown great potential to boost the end-to-end transmission performance. To further improve the system efficiency, in this paper, we propose a class of…
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding
Jincheng Dai, Ping Zhang, Kai Niu +3
Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable co…