Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Xu Linli"'
Publikováno v:
Acta Biochimica et Biophysica Sinica, Vol 55, Pp 1864-1873 (2023)
DNA double-strand break (DSB) repair by homologous recombination (HR) is crucial for the maintenance of genome stability and integrity. In this study, we aim to identify novel RNA binding proteins (RBPs) involved in HR repair because little is known
Externí odkaz:
https://doaj.org/article/2e394efa880b41eb91d4a7285c090c09
Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencoders like VQGAN or VAE to encod
Externí odkaz:
http://arxiv.org/abs/2410.12490
Large vision-language models (LVLMs) integrate visual information into large language models, showcasing remarkable multi-modal conversational capabilities. However, the visual modules introduces new challenges in terms of robustness for LVLMs, as at
Externí odkaz:
http://arxiv.org/abs/2410.06699
In-context learning for vision data has been underexplored compared with that in natural language. Previous works studied image in-context learning, urging models to generate a single image guided by demonstrations. In this paper, we propose and stud
Externí odkaz:
http://arxiv.org/abs/2407.07356
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction
Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information pr
Externí odkaz:
http://arxiv.org/abs/2406.12707
While recent advancements in speech language models have achieved significant progress, they face remarkable challenges in modeling the long acoustic sequences of neural audio codecs. In this paper, we introduce \textbf{G}enerative \textbf{P}re-train
Externí odkaz:
http://arxiv.org/abs/2406.00976
Autor:
Liu, Chaohu, Yin, Kun, Cao, Haoyu, Jiang, Xinghua, Li, Xin, Liu, Yinsong, Jiang, Deqiang, Sun, Xing, Xu, Linli
Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance across various tasks. However, their performance in visual document und
Externí odkaz:
http://arxiv.org/abs/2404.06918
Autor:
Cheng, Yifei, Shen, Li, Xu, Linli, Qian, Xun, Wu, Shiwei, Zhou, Yiming, Zhang, Tie, Tao, Dacheng, Chen, Enhong
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compressio
Externí odkaz:
http://arxiv.org/abs/2402.11857
While Diffusion Generative Models have achieved great success on image generation tasks, how to efficiently and effectively incorporate them into speech generation especially translation tasks remains a non-trivial problem. Specifically, due to the l
Externí odkaz:
http://arxiv.org/abs/2310.17570
Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts have been made to explore the complementary effect among multiple mod
Externí odkaz:
http://arxiv.org/abs/2307.09721