8 citations · 10 across the 8 of their papers we have counts for
8 papers
Quantize the Target, Quantize the Drafter: Efficient Inference with Qwen3.5-4B
Jaeyeon Kim, Jewon Lee, Bo-Kyeong Kim
This report describes our approach to the Efficient Qwen Competition, where the goal is to enable low-latency serving of Qwen3.5-4B on a resource-constrained NVIDIA A10G GPU. Our s…
ERGO: Efficient High-Resolution Visual Understanding for Vision-Language Models
Jewon Lee, Wooksu Shin, Seungmin Yang +5
Efficient processing of high-resolution images is crucial for real-world vision-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial comp…
When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs
Hiskias Dingeto, Taeyoun Kwon, Dasol Choi +5
As large language models (LLMs) become increasingly integrated into daily life, audio has emerged as a key interface for human-AI interaction. However, this convenience also introd…
Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features
Jewon Lee, Ki-Ung Song, Seungmin Yang +6
Visual token reduction lowers inference costs caused by extensive image features in large vision-language models (LVLMs). Unlike relevant studies that prune tokens in self-attentio…
Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation
Kangyeol Kim, Wooseok Seo, Sehyun Nam +5
Personalized text-to-image (P-T2I) generation aims to create new, text-guided images featuring the personalized subject with a few reference images. However, balancing the trade-of…
LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Thibault Castells, Hyoung-Kyu Song, Bo-Kyeong Kim +1
Latent Diffusion Models (LDMs) have emerged as powerful generative models, known for delivering remarkable results under constrained computational resources. However, deploying LDM…
EdgeFusion: On-Device Text-to-Image Generation
Thibault Castells, Hyoung-Kyu Song, Tairen Piao +6
The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application. To tackle this challenge, recent…
Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods
Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim +4
Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs. Width pruning reduces the size of projection weight mat…