1 citations · 1 across the 4 of their papers we have counts for
4 papers
QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization
Yuhao Xu, Yantai Yang, Zhenyang Fan +4
The advent of Vision-Language-Action (VLA) models represents a significant leap for embodied intelligence, yet their immense computational demands critically hinder deployment on r…
AutoPrune: Each Complexity Deserves a Pruning Policy
Hanshi Wang, Yuhao Xu, Zekun Xu +5
The established redundancy in visual tokens within large vision-language models allows pruning to effectively reduce their substantial computational demands. Previous methods typic…
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Manyi Li, Renshuai Tao, Yufan Liu +5
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have beco…
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
Wenyang Luo, Haina Qin, Zewen Chen +6
Image restoration tasks like deblurring, denoising, and dehazing usually need distinct models for each degradation type, restricting their generalization in real-world scenarios wi…