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cs.CV2025

Benchmarking and Enhancing VLM for Compressed Image Understanding

Zifu Zhang, Tongda Xu, Siqi Li +4

With the rapid development of Vision-Language Models (VLMs) and the growing demand for their applications, efficient compression of the image inputs has become increasingly importa…

cs.LG2025

Training-Free Vector Quantization via Gaussian VAEs

Tongda Xu, Wendi Zheng, Jiajun He +4

Vector-quantized variational autoencoders (VQ-VAEs) are discrete autoencoders that compress images into discrete tokens. However, they are difficult to train due to discretization.…

cs.CV2025

V-Shuffle: Zero-Shot Style Transfer via Value Shuffle

Haojun Tang, Qiwei Lin, Tongda Xu +2

Attention injection-based style transfer has achieved remarkable progress in recent years. However, existing methods often suffer from content leakage, where the undesired semantic…

cs.CV2025

Versatile Recompression-Aware Perceptual Image Super-Resolution

Mingwei He, Tongda Xu, Xingtong Ge +3

Perceptual image super-resolution (SR) methods restore degraded images and produce sharp outputs. In practice, those outputs are usually recompressed for storage and transmission.…

cs.CV2025

PICD: Versatile Perceptual Image Compression with Diffusion Rendering

Tongda Xu, Jiahao Li, Bin Li +3

Recently, perceptual image compression has achieved significant advancements, delivering high visual quality at low bitrates for natural images. However, for screen content, existi…

cs.CV2025

CoopDETR: A Unified Cooperative Perception Framework for 3D Detection via Object Query

Zhe Wang, Shaocong Xu, Xucai Zhuang +5

Cooperative perception enhances the individual perception capabilities of autonomous vehicles (AVs) by providing a comprehensive view of the environment. However, balancing percept…