Direct Assessment
Direct assessment encompasses a broad range of techniques for evaluating diverse systems and phenomena, from the psychological traits of language models to the precision of 3D models and the performance of autonomous vehicles. Current research focuses on developing robust and reliable assessment methods, often employing machine learning models like VQ-VAEs, various neural networks (including vision transformers and graph neural networks), and large language models (LLMs) for automated analysis and evaluation. These advancements are crucial for improving the trustworthiness and reliability of AI systems, enhancing diagnostic capabilities in healthcare, and optimizing performance in various engineering and scientific domains.
Papers
Auto-assessment of assessment: A conceptual framework towards fulfilling the policy gaps in academic assessment practices
Wasiq Khan, Luke K. Topham, Peter Atherton, Raghad Al-Shabandar, Hoshang Kolivand, Iftikhar Khan, Abir Hussain
CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
Jiewen Yang, Yiqun Lin, Bin Pu, Jiarong Guo, Xiaowei Xu, Xiaomeng Li