1 citations · 2 across the 11 of their papers we have counts for
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Mitigating Information Loss under High Pruning Rates for Efficient Large Vision Language Models
Mingyu Fu, Wei Suo, Ji Ma +3
Despite the great success of Large Vision Language Models (LVLMs), their high computational cost severely limits their broad applications. The computational cost of LVLMs mainly st…
Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers
Ji Ma, Wei Suo, Peng Wang +1
Although large vision-language models (LVLMs) have demonstrated impressive capabilities in multi-modal understanding and reasoning, their practical applications are still limited b…
Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning
Haizhao Jing, Haokui Zhang, Zhenhao Shang +3
Neural Architecture Representation Learning aims to transform network models into feature representations for predicting network attributes, playing a crucial role in deploying and…
DiffV2IR: Visible-to-Infrared Diffusion Model via Vision-Language Understanding
Lingyan Ran, Lidong Wang, Guangcong Wang +2
The task of translating visible-to-infrared images (V2IR) is inherently challenging due to three main obstacles: 1) achieving semantic-aware translation, 2) managing the diverse wa…
Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
Wei Suo, Lijun Zhang, Mengyang Sun +3
Large Vision-Language Models (LVLMs) have obtained impressive performance in visual content understanding and multi-modal reasoning. Unfortunately, these large models suffer from s…