Zobrazeno 1 - 10
of 310 836
pro vyhledávání: '"Pang AN"'
Autor:
Wu, Feize, Pang, Yun, Zhang, Junyi, Pang, Lianyu, Yin, Jian, Zhao, Baoquan, Li, Qing, Mao, Xudong
Recent advances in text-to-image personalization have enabled high-quality and controllable image synthesis for user-provided concepts. However, existing methods still struggle to balance identity preservation with text alignment. Our approach is bas
Externí odkaz:
http://arxiv.org/abs/2408.15914
In recent years, artificial intelligence (AI) has become increasingly integrated into education, reshaping traditional learning environments. Despite this, there has been limited investigation into fully operational artificial human lecturers. To the
Externí odkaz:
http://arxiv.org/abs/2410.03525
The black-box nature of large language models (LLMs) poses challenges in interpreting results, impacting issues such as data intellectual property protection and hallucination tracing. Training data attribution (TDA) methods are considered effective
Externí odkaz:
http://arxiv.org/abs/2410.01285
Autor:
Lyu, Weimin, Yao, Jiachen, Gupta, Saumya, Pang, Lu, Sun, Tao, Yi, Lingjie, Hu, Lijie, Ling, Haibin, Chen, Chao
The emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descriptions from visual inputs. Despite their growing importance, the secu
Externí odkaz:
http://arxiv.org/abs/2410.01264
Autor:
Sun, Haotian, Zhang, Bowen, Li, Yanghao, Huang, Haoshuo, Lei, Tao, Pang, Ruoming, Dai, Bo, Du, Nan
Diffusion transformers have been widely adopted for text-to-image synthesis. While scaling these models up to billions of parameters shows promise, the effectiveness of scaling beyond current sizes remains underexplored and challenging. By explicitly
Externí odkaz:
http://arxiv.org/abs/2410.02098
Autor:
Feng, Shengyu, Kong, Xiang, Ma, Shuang, Zhang, Aonan, Yin, Dong, Wang, Chong, Pang, Ruoming, Yang, Yiming
Augmenting the multi-step reasoning abilities of Large Language Models (LLMs) has been a persistent challenge. Recently, verification has shown promise in improving solution consistency by evaluating generated outputs. However, current verification a
Externí odkaz:
http://arxiv.org/abs/2410.01920
Autor:
Song, Linke, Pang, Zixuan, Wang, Wenhao, Wang, Zihao, Wang, XiaoFeng, Chen, Hongbo, Song, Wei, Jin, Yier, Meng, Dan, Hou, Rui
The wide deployment of Large Language Models (LLMs) has given rise to strong demands for optimizing their inference performance. Today's techniques serving this purpose primarily focus on reducing latency and improving throughput through algorithmic
Externí odkaz:
http://arxiv.org/abs/2409.20002
Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generat
Externí odkaz:
http://arxiv.org/abs/2409.19656
The emergence of Vision Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to produce detailed text descriptions based on visual inputs, yet it introduces new security vulnerabilities.
Externí odkaz:
http://arxiv.org/abs/2409.19232
Cone Beam Computed Tomography (CBCT) finds diverse applications in medicine. Ensuring high image quality in CBCT scans is essential for accurate diagnosis and treatment delivery. Yet, the susceptibility of CBCT images to noise and artifacts undermine
Externí odkaz:
http://arxiv.org/abs/2409.18355