December 29, 2024

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References Bias is commonly identified in medical image analysis applications [38,40]. For instance [6], a CNN trained on brain MRI resulted in a significant difference between ethnicities. Seyyed-Kalantari et al. [32] observed that minorities received higher rates of algorithmic underdiagnosis. Zong et al. [40] assessed bias mitigation algorithms inand out-of-distribution settings. The

Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 5. Conclusion, Limitation, and Future Works We introduce LLaVA-Phi, a vision language assistant developed using the compact language model Phi-2. Our work demonstrates that such small vision-language models can perform effectively on standard benchmarks when combined with the LLaVA training methodology and a select dataset of high-quality

In a world dominated by connectivity, we rely on GPS for everything from navigating city streets to tracking the arrival of our food delivery. But most of us don’t give much thought to how it all works—until it doesn’t. Whether it’s your Uber driver getting lost in a crowded urban area, your delivery package arriving late, or an autonomous vehicle encountering a signal disruption, the vulnerabilities of GPS are becoming clearer. As we move toward

Authors: (1) Yichen Zhu, Midea Group; (2) Minjie Zhu, Midea Group and East China Normal University; (3) Ning Liu, Midea Group; (4) Zhicai Ou, Midea Group; (5) Xiaofeng Mou, Midea Group. Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 3.2. Qualitative Results We present several examples that demonstrate the remarkable generalization capabilities of LLaVA-Phi, comparing