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pro vyhledávání: '"Chua, Tat Seng"'
Autor:
Cui, Chenhang, Deng, Gelei, Zhang, An, Zheng, Jingnan, Li, Yicong, Gao, Lianli, Zhang, Tianwei, Chua, Tat-Seng
Recent advances in Large Vision-Language Models (LVLMs) have showcased strong reasoning abilities across multiple modalities, achieving significant breakthroughs in various real-world applications. Despite this great success, the safety guardrail of
Externí odkaz:
http://arxiv.org/abs/2411.11496
Autor:
Ren, Ruiyang, Wang, Yuhao, Zhou, Kun, Zhao, Wayne Xin, Wang, Wenjie, Liu, Jing, Wen, Ji-Rong, Chua, Tat-Seng
Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passages are reranked in a listwise manner and a textual reranked permutati
Externí odkaz:
http://arxiv.org/abs/2411.04602
Remote sensing change detection aims to perceive changes occurring on the Earth's surface from remote sensing data in different periods, and feed these changes back to humans. However, most existing methods only focus on detecting change regions, lac
Externí odkaz:
http://arxiv.org/abs/2410.23828
Autor:
Huang, Youcheng, Zhu, Fengbin, Tang, Jingkun, Zhou, Pan, Lei, Wenqiang, Lv, Jiancheng, Chua, Tat-Seng
Visual Language Models (VLMs) are vulnerable to adversarial attacks, especially those from adversarial images, which is however under-explored in literature. To facilitate research on this critical safety problem, we first construct a new laRge-scale
Externí odkaz:
http://arxiv.org/abs/2410.22888
Autor:
Zhang, Yang, You, Juntao, Bai, Yimeng, Zhang, Jizhi, Bao, Keqin, Wang, Wenjie, Chua, Tat-Seng
Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequen
Externí odkaz:
http://arxiv.org/abs/2410.22809
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Autor:
Zhu, Fengbin, Liu, Ziyang, Ng, Xiang Yao, Wu, Haohui, Wang, Wenjie, Feng, Fuli, Wang, Chao, Luan, Huanbo, Chua, Tat Seng
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated. Existing benchmarks either contain limited fine-grai
Externí odkaz:
http://arxiv.org/abs/2410.21311
Autor:
Cai, Hongru, Li, Yongqi, Wang, Wenjie, Zhu, Fengbin, Shen, Xiaoyu, Li, Wenjie, Chua, Tat-Seng
Web agents have emerged as a promising direction to automate Web task completion based on user instructions, significantly enhancing user experience. Recently, Web agents have evolved from traditional agents to Large Language Models (LLMs)-based Web
Externí odkaz:
http://arxiv.org/abs/2410.17236
Autor:
Cui, Chenhang, Zhang, An, Zhou, Yiyang, Chen, Zhaorun, Deng, Gelei, Yao, Huaxiu, Chua, Tat-Seng
The recent advancements in large language models (LLMs) and pre-trained vision models have accelerated the development of vision-language large models (VLLMs), enhancing the interaction between visual and linguistic modalities. Despite their notable
Externí odkaz:
http://arxiv.org/abs/2410.14148
Autor:
Lu, Kangkang, Yu, Yanhua, Huang, Zhiyong, Li, Jia, Wang, Yuling, Liang, Meiyu, Qin, Xiting, Ren, Yimeng, Chua, Tat-Seng, Wang, Xidian
Graph Neural Networks (GNNs) have garnered significant scholarly attention for their powerful capabilities in modeling graph structures. Despite this, two primary challenges persist: heterogeneity and heterophily. Existing studies often address heter
Externí odkaz:
http://arxiv.org/abs/2410.13373
Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained attention in recommendation for their ability to model complex distribut
Externí odkaz:
http://arxiv.org/abs/2410.13117