Zobrazeno 1 - 10
of 11 281
pro vyhledávání: '"CHENG, HONG"'
Autor:
Huang, Minbin, Huang, Runhui, Shi, Han, Chen, Yimeng, Zheng, Chuanyang, Sun, Xiangguo, Jiang, Xin, Li, Zhenguo, Cheng, Hong
The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a range of downstream applications, such as visual question answering an
Externí odkaz:
http://arxiv.org/abs/2411.17773
Autor:
Zhang, Hengyu, Shen, Chunxu, Sun, Xiangguo, Tan, Jie, Rong, Yu, Piao, Chengzhi, Cheng, Hong, Yi, Lingling
In the online digital world, users frequently engage with diverse items across multiple domains (e.g., e-commerce platforms, streaming services, and social media networks), forming complex heterogeneous interaction graphs. Leveraging this multi-domai
Externí odkaz:
http://arxiv.org/abs/2410.11719
Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal approaches. The challenges of aligning diverse data in early fusion and
Externí odkaz:
http://arxiv.org/abs/2410.08739
In recent years, graph prompting has emerged as a promising research direction, enabling the learning of additional tokens or subgraphs appended to the original graphs without requiring retraining of pre-trained graph models across various applicatio
Externí odkaz:
http://arxiv.org/abs/2410.01635
Large Language Models (LLMs) have greatly contributed to the development of adaptive intelligent agents and are positioned as an important way to achieve Artificial General Intelligence (AGI). However, LLMs are prone to produce factually incorrect in
Externí odkaz:
http://arxiv.org/abs/2408.07611
Detecting objects seamlessly blended into their surroundings represents a complex task for both human cognitive capabilities and advanced artificial intelligence algorithms. Currently, the majority of methodologies for detecting camouflaged objects m
Externí odkaz:
http://arxiv.org/abs/2407.13133
Individual personalities significantly influence our perceptions, decisions, and social interactions, which is particularly crucial for gaining insights into human behavior patterns in online social network analysis. Many psychological studies have o
Externí odkaz:
http://arxiv.org/abs/2407.03568
Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot set
Externí odkaz:
http://arxiv.org/abs/2406.05346
Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences, which can be categorized into two primary training paradigms: supervised learning and self-supervised learning. However
Externí odkaz:
http://arxiv.org/abs/2405.20606
Autor:
Wu, Xixi, Shen, Yifei, Shan, Caihua, Song, Kaitao, Wang, Siwei, Zhang, Bohang, Feng, Jiarui, Cheng, Hong, Chen, Wei, Xiong, Yun, Li, Dongsheng
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural language into solvable sub-tasks, thereby fulfilling the ori
Externí odkaz:
http://arxiv.org/abs/2405.19119