Evelyn Zhang — accepted AI-conference papers

2026

D²Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning

Evelyn Zhang, Fufu Yu, Aoqi Wu, Zichen Wen, Ke Yan, Shouhong Ding +2

AAAI 2026technical

Processing long visual token sequences poses a significant computational burden on Multimodal Large Language Models (MLLMs). While token pruning offers a path to acceleration, we find that current methods, while adequate for general understanding, catastrophically fail on fine-grained localization t

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2025

Training-Free and Hardware-Friendly Acceleration for Diffusion Models via Similarity-based Token Pruning

Evelyn Zhang, Jiayi Tang, Xuefei Ning, Linfeng Zhang

AAAI 2025technical

The excellent performance of diffusion models in image generation is always accompanied by overlarge computation costs, which have prevented the application of diffusion models in edge devices and interactive applications. Previous works mainly focus on using fewer sampling steps and compressing the…

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