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of 2 459
pro vyhledávání: '"multimodal benchmark"'
We introduce BackdoorMBTI, the first backdoor learning toolkit and benchmark designed for multimodal evaluation across three representative modalities from eleven commonly used datasets. BackdoorMBTI provides a systematic backdoor learning pipeline,
Externí odkaz:
http://arxiv.org/abs/2411.11006
Benefiting from the revolutionary advances in large language models (LLMs) and foundational vision models, large vision-language models (LVLMs) have also made significant progress. However, current benchmarks focus on tasks that evaluating only a sin
Externí odkaz:
http://arxiv.org/abs/2410.12564
Autor:
Cheng, Jiali, Amiri, Hadi
Recent advancements in Machine Unlearning (MU) have introduced solutions to selectively remove certain training samples, such as those with outdated or sensitive information, from trained models. Despite these advancements, evaluation of MU methods h
Externí odkaz:
http://arxiv.org/abs/2406.14796
Autor:
Ying, Kaining, Meng, Fanqing, Wang, Jin, Li, Zhiqian, Lin, Han, Yang, Yue, Zhang, Hao, Zhang, Wenbo, Lin, Yuqi, Liu, Shuo, Lei, Jiayi, Lu, Quanfeng, Chen, Runjian, Xu, Peng, Zhang, Renrui, Zhang, Haozhe, Gao, Peng, Wang, Yali, Qiao, Yu, Luo, Ping, Zhang, Kaipeng, Shao, Wenqi
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimodal evaluation benchmarks cover a limited number of multimodal tasks te
Externí odkaz:
http://arxiv.org/abs/2404.16006
Autor:
Doris, Anna C., Grandi, Daniele, Tomich, Ryan, Alam, Md Ferdous, Ataei, Mohammadmehdi, Cheong, Hyunmin, Ahmed, Faez
This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation. Developed with a focus on real-wor
Externí odkaz:
http://arxiv.org/abs/2404.07917
Autor:
Jin, Congyun, Zhang, Ming, Ma, Xiaowei, Yujiao, Li, Wang, Yingbo, Jia, Yabo, Du, Yuliang, Sun, Tao, Wang, Haowen, Fan, Cong, Gu, Jinjie, Chi, Chenfei, Lv, Xiangguo, Li, Fangzhou, Xue, Wei, Huang, Yiran
Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing
Externí odkaz:
http://arxiv.org/abs/2402.14840
Akademický článek
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Akademický článek
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Autor:
Qi, Peng, Bu, Yuyan, Cao, Juan, Ji, Wei, Shui, Ruihao, Xiao, Junbin, Wang, Danding, Chua, Tat-Seng
Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detection has recently received a lot of attention. Existing works face two r
Externí odkaz:
http://arxiv.org/abs/2211.10973
Autor:
Sharma, Vasu, Goyal, Prasoon, Lin, Kaixiang, Thattai, Govind, Gao, Qiaozi, Sukhatme, Gaurav S.
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich m
Externí odkaz:
http://arxiv.org/abs/2208.13626