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7. BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models

Author

Cai, Rizhao, Song, Zirui, Guan, Dayan, Chen, Zhenhao, Luo, Xing, Yi, Chenyu, and Kot, Alex

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable capabilities in visual reasoning with common image styles. However, their robustness against diverse style shifts, crucial for practical applications, remains largely unexplored. In this paper, we propose a new benchmark, BenchLMM, to assess the robustness of LMMs against three different styles: artistic image style, imaging sensor style, and application style, where each style has five sub-styles. Utilizing BenchLMM, we comprehensively evaluate state-of-the-art LMMs and reveal: 1) LMMs generally suffer performance degradation when working with other styles; 2) An LMM performs better than another model in common style does not guarantee its superior performance in other styles; 3) LMMs' reasoning capability can be enhanced by prompting LMMs to predict the style first, based on which we propose a versatile and training-free method for improving LMMs; 4) An intelligent LMM is expected to interpret the causes of its errors when facing stylistic variations. We hope that our benchmark and analysis can shed new light on developing more intelligent and versatile LMMs., Comment: Code is available at https://github.com/AIFEG/BenchLMM

Published
2023

10. Health economics study of paliperidone palmitate in the treatment of schizophrenia: a 12-month cohort study

Author

Luo, Xing, Liu, Fang, Lu, Jin, Cheng, Yuqi, Xu, Xiufeng, He, Xiaolin, Xia, Yongbing, Gao, Changqing, Xie, Xian, Zhao, Yu, Gao, Chunqiang, Ding, Hua, He, Yuefei, Zhang, Lifen, Zhang, Xi, Song, Jianhui, Yang, Shunying, Liu, Liming, Chen, Wenming, Liu, Wei, Luo, Chuanlin, Pu, Ensheng, Lei, Ming, Wang, Yan, Sun, Zanzong, Yang, Rucheng, Zhou, Yong, Zhu, Xianrong, Wang, Bo, He, Shuhua, Gao, Donghua, Li, Zhongcai, Huang, Liqiong, Wang, Tianlan, Yang, Guangya, Liu, Hong, Zhao, Jinkun, and Wang, Jicai

Published
2024
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20. Effect of ultra-high-speed laser melting deposition process on performance of Al-Mg-Sc high-strength aluminum alloy

Author

WEN Pin, DONG Ziliang, WU Yu, DONG Dingyang, LUO Xing, DU Borui, XU Peixin, and CHEN Fei

Subjects
ultra-high speed laser melting deposition, high strength aluminum alloy, mechanical properties, numerical simulation, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

In order to clarify the microstructure of Al-Mg-Sc high-strength aluminum alloy prepared by ultra-high speed laser melting deposition，and study the relationship between structure and properties，Al-Mg-Sc high-strength aluminum alloy was prepared by using 7075 aluminum alloy as matrix and self-developed equipment（LDF3000-40 laser melting deposition machine）. The effect of laser scanning rate on the microstructure and tensile properties of the materials was investigated. The results show that there are no obvious defects such as pores and cracks in the samples deposited by ultra-high speed laser melting，but contain a few small keyholes. The samples are composed of fine α-Al equiaxial crystals and dispersed Al3（Sc，Zr）particles. The effect of scanning rate on mechanical properties is further studied by numerical simulation. It is found that the faster scanning rate of laser in the range of 0.1-1 m/s can reduce the accumulation of powder materials and porosity of the surface of the deposition layer，thus the mechanical properties can be improved. The maximum tensile strength is 303 MPa and elongation at break is 22.5%.

Published
2024
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33. An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation

Author

Luo, Xing and Zhang, Dongxiao

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Physics - Computational Physics, Physics - Data Analysis, Statistics and Probability
Abstract

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports the development of data based PVPG forecasting. Although emerging deep learning (DL) models, such as the long short-term memory (LSTM) model, based on historical data, have provided effective solutions for PVPG forecasting with great successes, these models utilize offline learning. As a result, DL models cannot take advantage of the opportunity to learn from newly-arrived data, and are unable to handle concept drift caused by installing extra PV units and unforeseen PV unit failures. Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM) model, which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. A two-phase adaptive learning strategy (TP-ALS) is integrated into AD-LSTM, and a sliding window (SDWIN) algorithm is proposed, to detect concept drift in PV systems. Multiple datasets from PV systems are utilized to assess the feasibility and effectiveness of the proposed approaches. The developed AD-LSTM model demonstrates greater forecasting capability than the offline LSTM model, particularly in the presence of concept drift. Additionally, the proposed AD-LSTM model also achieves superior performance in terms of day-ahead PVPG forecasting compared to other traditional machine learning models and statistical models in the literature., Comment: 26 pages, 10 figures

Published
2021
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