Zobrazeno 1 - 10
of 671
pro vyhledávání: '"Wu, Xiao Ming"'
We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules, thereby facilitating the seamless integration of content understanding
Externí odkaz:
http://arxiv.org/abs/2412.15973
Autor:
Liu, Bo, Zou, Ke, Zhan, Liming, Lu, Zexin, Dong, Xiaoyu, Chen, Yidi, Xie, Chengqiang, Cao, Jiannong, Wu, Xiao-Ming, Fu, Huazhu
Medical Visual Question Answering (VQA) is an essential technology that integrates computer vision and natural language processing to automatically respond to clinical inquiries about medical images. However, current medical VQA datasets exhibit two
Externí odkaz:
http://arxiv.org/abs/2411.16778
Autor:
Zhao, Xiangyu, Zhou, Zhiwang, Zhang, Wenlong, Liu, Yihao, Chen, Xiangyu, Gong, Junchao, Chen, Hao, Fei, Ben, Chen, Shiqi, Ouyang, Wanli, Wu, Xiao-Ming, Bai, Lei
The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven models focus on single weather understanding tasks (e.g., weather forec
Externí odkaz:
http://arxiv.org/abs/2411.05420
Equipping humanoid robots with the capability to understand emotional states of human interactants and express emotions appropriately according to situations is essential for affective human-robot interaction. However, enabling current vision-aware m
Externí odkaz:
http://arxiv.org/abs/2410.18373
Autor:
Shi, Guangyuan, Lu, Zexin, Dong, Xiaoyu, Zhang, Wenlong, Zhang, Xuanyu, Feng, Yujie, Wu, Xiao-Ming
Aligning large language models (LLMs) through fine-tuning is essential for tailoring them to specific applications. Therefore, understanding what LLMs learn during the alignment process is crucial. Recent studies suggest that alignment primarily adju
Externí odkaz:
http://arxiv.org/abs/2410.17875
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, this way explicitly o
Externí odkaz:
http://arxiv.org/abs/2410.06521
Cognitive biases are systematic deviations in thinking that lead to irrational judgments and problematic decision-making, extensively studied across various fields. Recently, large language models (LLMs) have shown advanced understanding capabilities
Externí odkaz:
http://arxiv.org/abs/2409.16022
Autor:
Liu, Bo, Zhan, Liming, Feng, Yujie, Lu, Zexin, Xie, Chengqiang, Xue, Lei, Lam, Albert Y. S., Wu, Xiao-Ming
In the realm of task-oriented dialogue systems, a robust intent detection mechanism must effectively handle malformed utterances encountered in real-world scenarios. This study presents a novel fine-tuning framework for large language models (LLMs) a
Externí odkaz:
http://arxiv.org/abs/2409.11114
Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish between items, which can hinder their ability to effectively leverage item content information and generalize to long-tail or cold-start items. Recently, sem
Externí odkaz:
http://arxiv.org/abs/2409.07276
The fashion domain encompasses a variety of real-world multimodal tasks, including multimodal retrieval and multimodal generation. The rapid advancements in artificial intelligence generated content, particularly in technologies like large language m
Externí odkaz:
http://arxiv.org/abs/2408.11305