Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Zeng, Xingyu"'
Autor:
Fang, Rongyao, Duan, Chengqi, Wang, Kun, Li, Hao, Tian, Hao, Zeng, Xingyu, Zhao, Rui, Dai, Jifeng, Li, Hongsheng, Liu, Xihui
Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimodal large language models (MLLMs) for visual content generation. Howev
Externí odkaz:
http://arxiv.org/abs/2410.13861
Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive advancements in language models, a crucial question arises: can they
Externí odkaz:
http://arxiv.org/abs/2406.16605
Autor:
Chen, Sirui, Peng, Bo, Chen, Meiqi, Wang, Ruiqi, Xu, Mengying, Zeng, Xingyu, Zhao, Rui, Zhao, Shengjie, Qiao, Yu, Lu, Chaochao
Causal reasoning is viewed as crucial for achieving human-level machine intelligence. Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal
Externí odkaz:
http://arxiv.org/abs/2405.00622
Autor:
Kong, Yilun, Ruan, Jingqing, Chen, Yihong, Zhang, Bin, Bao, Tianpeng, Shi, Shiwei, Du, Guoqing, Hu, Xiaoru, Mao, Hangyu, Li, Ziyue, Zeng, Xingyu, Zhao, Rui
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.
Externí odkaz:
http://arxiv.org/abs/2311.11315
As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for customizing models and managing their outputs. However, the challenge of
Externí odkaz:
http://arxiv.org/abs/2311.09773
Active learning strategies aim to train high-performance models with minimal labeled data by selecting the most informative instances for labeling. However, existing methods for assessing data informativeness often fail to align directly with task mo
Externí odkaz:
http://arxiv.org/abs/2310.08387
Autor:
Ruan, Jingqing, Chen, Yihong, Zhang, Bin, Xu, Zhiwei, Bao, Tianpeng, Du, Guoqing, Shi, Shiwei, Mao, Hangyu, Li, Ziyue, Zeng, Xingyu, Zhao, Rui
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the intrinsic generative abilities of LLMs may prove insufficient for han
Externí odkaz:
http://arxiv.org/abs/2308.03427
Publikováno v:
CVPR2023 Workshop on Generative Models for Computer Vision
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance of FSOD model. Recent text-to-image generation models have shown promis
Externí odkaz:
http://arxiv.org/abs/2303.13221
Autor:
Jin, Guoqiang, Yang, Fan, Sun, Mingshan, Zhao, Ruyi, Liu, Yakun, Li, Wei, Bao, Tianpeng, Wu, Liwei, Zeng, Xingyu, Zhao, Rui
Self-supervised pre-training and transformer-based networks have significantly improved the performance of object detection. However, most of the current self-supervised object detection methods are built on convolutional-based architectures. We beli
Externí odkaz:
http://arxiv.org/abs/2303.08481
Publikováno v:
CVPR2023 Workshop on Learning with Limited Labelled Data
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. However, detecting novel categories with only a few samples usually leads to the problem of misclassification. In FSOD, w
Externí odkaz:
http://arxiv.org/abs/2302.14452