Zobrazeno 1 - 10
of 2 159
pro vyhledávání: '"Huang Xiaowei"'
Autor:
Huang Xiaowei, Zhao Wanying, Sun Wei, Li Zhihua, Zhang Ning, Shi Jiyong, Zhang Yang, Zhang Xinai, Shen Tingting, Zou Xiaobo
Publikováno v:
Food Chemistry: X, Vol 21, Iss , Pp 101054- (2024)
A ratiometric fluorescence sensor platform with easy-to-use and accurate is nanoengineered for NH3 quantitative detection and visual real-time monitoring of chicken freshness using smartphones. The ratiometric fluorescent probe formed by combining th
Externí odkaz:
https://doaj.org/article/392679d93ef6439582ae053e50ae0126
Autor:
Li Chuang, Shi Jiyong, Zhou Chenguang, Huang Xiaowei, Zhai Xiaodong, Yang Zhikun, Li Zhihua, Hu Xuetao, Li Yanxiao, Xiao Jianbo, Zou Xiaobo
Publikováno v:
Food Chemistry: X, Vol 20, Iss , Pp 100885- (2023)
In this study, beef was marinated with different low-sodium salt substitutes and heated and aged by employing superheated steam roasting and traditional roasting to investigate the effects of the various substitutes on the physicochemical properties,
Externí odkaz:
https://doaj.org/article/674810c3a2934430b98480ae5afe2d67
Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and enabling the efficient training of large models. However, current resea
Externí odkaz:
http://arxiv.org/abs/2411.09265
In this paper, we propose a novel framework for enhancing visual comprehension in autonomous driving systems by integrating visual language models (VLMs) with additional visual perception module specialised in object detection. We extend the Llama-Ad
Externí odkaz:
http://arxiv.org/abs/2411.05898
Few-shot Class Incremental Learning (FSCIL) presents a challenging yet realistic scenario, which requires the model to continually learn new classes with limited labeled data (i.e., incremental sessions) while retaining knowledge of previously learne
Externí odkaz:
http://arxiv.org/abs/2411.01172
Autor:
Wu, Sihao, Liu, Jiaxu, Yin, Xiangyu, Cheng, Guangliang, Zhao, Xingyu, Fang, Meng, Yi, Xinping, Huang, Xiaowei
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of purely data-driven methods. Current LLM-based agents require lengthy infe
Externí odkaz:
http://arxiv.org/abs/2410.12568
Autor:
Zhang, Yi, Chen, Zhen, Cheng, Chih-Hong, Ruan, Wenjie, Huang, Xiaowei, Zhao, Dezong, Flynn, David, Khastgir, Siddartha, Zhao, Xingyu
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of
Externí odkaz:
http://arxiv.org/abs/2409.18214
Data efficiency of learning, which plays a key role in the Reinforcement Learning (RL) training process, becomes even more important in continual RL with sequential environments. In continual RL, the learner interacts with non-stationary, sequential
Externí odkaz:
http://arxiv.org/abs/2408.13452
Guardrails have become an integral part of Large language models (LLMs), by moderating harmful or toxic response in order to maintain LLMs' alignment to human expectations. However, the existing guardrail methods do not consider different needs and a
Externí odkaz:
http://arxiv.org/abs/2408.08959
Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to its cost-effectiveness. The typical framework involves using image-level labels as training data to generate pixel-level pseudo-label
Externí odkaz:
http://arxiv.org/abs/2407.10649