Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Yu, Zhelun"'
Autor:
Shu, Fangxun, Liao, Yue, Zhuo, Le, Xu, Chenning, Zhang, Lei, Zhang, Guanghao, Shi, Haonan, Chen, Long, Zhong, Tao, He, Wanggui, Fu, Siming, Li, Haoyuan, Li, Bolin, Yu, Zhelun, Liu, Si, Li, Hongsheng, Jiang, Hao
We introduce LLaVA-MoD, a novel framework designed to enable the efficient training of small-scale Multimodal Language Models (s-MLLM) by distilling knowledge from large-scale MLLM (l-MLLM). Our approach tackles two fundamental challenges in MLLM dis
Externí odkaz:
http://arxiv.org/abs/2408.15881
Autor:
Lin, Tianwei, Liu, Jiang, Zhang, Wenqiao, Li, Zhaocheng, Dai, Yang, Li, Haoyuan, Yu, Zhelun, He, Wanggui, Li, Juncheng, Jiang, Hao, Tang, Siliang, Zhuang, Yueting
While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially in multidimensional task scenarios. To address this issue, one straig
Externí odkaz:
http://arxiv.org/abs/2408.09856
Autor:
He, Wanggui, Fu, Siming, Liu, Mushui, Wang, Xierui, Xiao, Wenyi, Shu, Fangxun, Wang, Yi, Zhang, Lei, Yu, Zhelun, Li, Haoyuan, Huang, Ziwei, Gan, LeiLei, Jiang, Hao
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis. In this work, we introduce MARS, a novel framework for T2I generation th
Externí odkaz:
http://arxiv.org/abs/2407.07614
Autor:
Xiao, Wenyi, Huang, Ziwei, Gan, Leilei, He, Wanggui, Li, Haoyuan, Yu, Zhelun, Jiang, Hao, Wu, Fei, Zhu, Linchao
The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly rest
Externí odkaz:
http://arxiv.org/abs/2404.14233
Autor:
Li, Haoyuan, Jiang, Hao, Zhang, Tianke, Yu, Zhelun, Yin, Aoxiong, Cheng, Hao, Fu, Siming, Zhang, Yuhao, He, Wanggui
Training AI models has always been challenging, especially when there is a need for custom models to provide personalized services. Algorithm engineers often face a lengthy process to iteratively develop models tailored to specific business requireme
Externí odkaz:
http://arxiv.org/abs/2311.06622
Publikováno v:
ISCAS
This paper presents a fast compressive sensing reconstruction algorithm implemented on FPGA using Orthogonal Matching Pursuit (OMP). The algorithm is optimized with QR decomposition to solve the least square problem and avoids the square root operati