The paper introduces a GAN‑based semantic communication framework for image transmission in the Internet of Vehicles, aiming to overcome bandwidth and channel limitations. At the transmitter, a pyramid attention network extracts semantic label maps and a priority mechanism assigns weights to categories based on driving safety, guiding bit allocation and loss design. The receiver reconstructs images using a coarse‑to‑fine multi‑resolution generator, multi‑scale discriminator, temporal consistency, spatial pyramid pooling, and class‑aware convolutions, achieving high‑fidelity results with combined adversarial, feature‑matching, and perceptual losses. Experiments on Cityscapes demonstrate superior semantic segmentation accuracy and image quality compared to existing methods, with stable performance under AWGN and Rayleigh channels.
arXiv:2608. 02289v1 Announce Type: cross Abstract: Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation.
The paper introduces KDG‑SemNOMA, a framework for 6G robotic vehicle networks that combines knowledge distillation and generative models to improve semantic communication over uplink non‑orthogonal multiple access (NOMA). It employs a ConvNeXt‑based deep joint source‑channel coding architecture with an enhanced attention feature module for dynamic channel adaptation, and uses an orthogonal teacher model to guide a NOMA student model via two‑stage knowledge distillation. A channel‑conditional GAN further refines the reconstructed images, yielding higher pixel‑level accuracy and perceptual fidelity on the FFHQ‑256 dataset compared to state‑of‑the‑art methods.
arXiv:2404. 06294v2 Announce Type: replace-cross Abstract: Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
Most existing extreme compression methods fail to achieve an optimal rate-distortion-perception trade-off, as they typically prioritize perceptual fidelity and visual realism over pixel-level accuracy. Consequently, the resulting reconstructions often deviate noticeably from the originals.
The paper examines whether standard image‑fidelity metrics (SSIM, PSNR, MSE) accurately reflect the performance of GAN‑generated synthetic sonar data in robotic perception tasks. Using a Pix2Pix GAN with four discriminator configurations (PixelGAN, PatchGAN‑16, PatchGAN‑70, ImageGAN), the authors train object detectors (YOLOX‑S, YOLOX‑L, Faster R‑CNN) solely on real sonar images and evaluate them on the synthetic outputs. Results show a mismatch: the discriminator that yields the best pixel‑level scores does not always produce the best detection performance, with PatchGAN models achieving strong downstream results despite lower SSIM/PSNR/MSE values.
arXiv:2606. 28416v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions.
arXiv:2608.28730v1 Announce Type: cross Abstract: Latest JPEG restoration systems achieve strong quality with large models, yet often remain too slow and expensive for efficient on-device deployment....
arXiv:2606. 25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs.
The paper introduces RAE-CoD, a diffusion-based compression method that operates in a representation autoencoder space to preserve recognizable content even at extremely low bitrates. It addresses the problem of semantic collapse observed in existing codecs when the bitrate approaches zero, showing that reconstruction losses conflict with semantic objectives and that VAE diffusion models lose efficiency in preserving semantics. Experiments on MSCOCO-30K demonstrate that RAE-CoD outperforms competitors, reducing VFM feature MSE and Fréchet Distance ratios by at least 25.7% and 69.1% at 0.001–0.008 bpp while maintaining stable recognizability and quality.
Semantically-Guided Domain Randomization (S‑GDR) is an annotation‑free pipeline that uses vision‑language model captioning of a small real reference set, diffusion‑based background synthesis, and mask‑based object composition to generate synthetic training data. In a high‑mix, low‑volume automotive detection benchmark, S‑GDR achieves a mAP50‑95 of 0.739 with only 200 synthetic images, outperforming a domain‑randomized render baseline and several other synthetic data methods under the same budget. These results suggest S‑GDR is a viable alternative for training visual perception systems when annotation, energy, and time resources are severely limited.