Benchmark Dataset
Benchmark datasets are curated collections of data designed to rigorously evaluate the performance of algorithms and models across various scientific domains. Current research focuses on developing datasets for diverse tasks, including multimodal data analysis (e.g., combining image, text, and audio data), challenging scenarios like low-resource languages or complex biological images, and addressing issues like model hallucinations and bias. These datasets are crucial for fostering objective comparisons, identifying limitations in existing methods, and driving advancements in machine learning and related fields, ultimately leading to more robust and reliable applications in diverse sectors.
Papers
SMiCRM: A Benchmark Dataset of Mechanistic Molecular Images
Ching Ting Leung, Yufan Chen, Hanyu Gao
MDS-ED: Multimodal Decision Support in the Emergency Department -- a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine
Juan Miguel Lopez Alcaraz, Hjalmar Bouma, Nils Strodthoff
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
Yi Zhang, Wang Zeng, Sheng Jin, Chen Qian, Ping Luo, Wentao Liu
MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes
Casper van Engelenburg, Fatemeh Mostafavi, Emanuel Kuhn, Yuntae Jeon, Michael Franzen, Matthias Standfest, Jan van Gemert, Seyran Khademi
Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines
Xinyi Ying, Chao Xiao, Ruojing Li, Xu He, Boyang Li, Zhaoxu Li, Yingqian Wang, Mingyuan Hu, Qingyu Xu, Zaiping Lin, Miao Li, Shilin Zhou, Wei An, Weidong Sheng, Li Liu
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
Zhengxin Yang, Wanling Gao, Luzhou Peng, Yunyou Huang, Fei Tang, Jianfeng Zhan