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20242026
most citedDynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation

4 citations · 8 across the 15 of their papers we have counts for

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cs.CV2024

A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs

Wangbo Zhao, Yizeng Han, Jiasheng Tang +5

Vision-language models (VLMs) have shown remarkable success across various multi-modal tasks, yet large VLMs encounter significant efficiency challenges due to processing numerous…

cs.CV2024

Dynamic Diffusion Transformer

Wangbo Zhao, Yizeng Han, Jiasheng Tang +5

Diffusion Transformer (DiT), an emerging diffusion model for image generation, has demonstrated superior performance but suffers from substantial computational costs. Our investiga…

cs.LG2024

Inference-Time Alignment of Diffusion Models with Direct Noise Optimization

Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang +3

In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives for downstream tasks, such as increasing…

cs.CV2024★ 4 cited

Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation

Wangbo Zhao, Jiasheng Tang, Yizeng Han +5

Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the e…

cs.LG2024★ 4 cited

Accelerating Parallel Sampling of Diffusion Models

Zhiwei Tang, Jiasheng Tang, Hao Luo +2

Diffusion models have emerged as state-of-the-art generative models for image generation. However, sampling from diffusion models is usually time-consuming due to the inherent auto…