no code implementations • 26 Mar 2023 • Sixian Wang, Jincheng Dai, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain.
no code implementations • 8 Nov 2022 • Jincheng Dai, Sixian Wang, Ke Yang, Kailin Tan, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain.
no code implementations • 4 Aug 2022 • Jincheng Dai, Ping Zhang, Kai Niu, Sixian Wang, Zhongwei Si, Xiaoqi Qin
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 communications.
no code implementations • 26 May 2022 • Sixian Wang, Jincheng Dai, Zijian Liang, Kai Niu, Zhongwei Si, Chao Dong, Xiaoqi Qin, Ping Zhang
In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels.
no code implementations • 26 May 2022 • Jun Wang, Sixian Wang, Jincheng Dai, Zhongwei Si, Dekun Zhou, Kai Niu
However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM).
1 code implementation • 21 Dec 2021 • Jincheng Dai, Sixian Wang, Kailin Tan, Zhongwei Si, Xiaoqi Qin, Kai Niu, Ping Zhang
In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding.
no code implementations • 9 Feb 2020 • Yifeng Ding, Shaoguo Wen, Jiyang Xie, Dongliang Chang, Zhanyu Ma, Zhongwei Si, Haibin Ling
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.