4 accepted papers
Diffusion models often exhibit inconsistent sample quality due to stochastic variations inherent in their sampling trajectories. Although training-based fine-tuning and inference-time alignment techniques aim to improve sample fidelity, they typically necessitate full denoising processes and externa
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). Previous works build upon convolutional neural networks (CNNs), which are inefficient in capturing global dependencies, resu…
In this paper, we propose a new class of high-efficiency semantic coded transmission methods to realize end-to-end speech transmission over wireless channels. We name the whole system as Deep Speech Semantic Transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech sour…
We study the problem of deep joint source-channel coding (D-JSCC) for correlated image sources, where each source is transmitted through a noisy independent channel to the common receiver. In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view tr…