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cs.CV2026
SAGE: Accelerating Vision-Language Models via Entropy-Guided Adaptive Speculative Decoding
Yujia Tong, Tian Zhang, Yunyang Wan +3
Speculative decoding has emerged as a promising approach to accelerate inference in vision-language models (VLMs) by enabling parallel verification of multiple draft tokens. Howeve…
cs.CV2025
LetheViT: Selective Machine Unlearning for Vision Transformers via Attention-Guided Contrastive Learning
Yujia Tong, Tian Zhang, Jingling Yuan +2
Vision Transformers (ViTs) have revolutionized computer vision tasks with their exceptional performance. However, the introduction of privacy regulations such as GDPR and CCPA has…
cs.CV2025
DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning
Yujia Tong, Jingling Yuan, Tian Zhang +2
Data-Free Quantization (DFQ) enables the quantization of Vision Transformers (ViTs) without requiring access to data, allowing for the deployment of ViTs on devices with limited re…