Large Scale Dataset
Large-scale datasets are crucial for training and evaluating advanced machine learning models across diverse scientific domains, driving progress in areas like computer vision, natural language processing, and genomics. Current research focuses on creating datasets for specific, challenging tasks, such as robust object detection in complex environments (e.g., underwater, cluttered scenes, low-light conditions), multimodal data integration (e.g., image-text, video-text), and handling long-range dependencies (e.g., in video grounding and temporal question answering). The availability of these high-quality, large datasets is essential for advancing model performance and enabling new applications in various fields, from autonomous driving and medical diagnosis to climate change modeling and personalized recommendations.
Papers
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Buyun Zhang, Liang Luo, Xi Liu, Jay Li, Zeliang Chen, Weilin Zhang, Xiaohan Wei, Yuchen Hao, Michael Tsang, Wenjun Wang, Yang Liu, Huayu Li, Yasmine Badr, Jongsoo Park, Jiyan Yang, Dheevatsa Mudigere, Ellie Wen
Multi-sensor large-scale dataset for multi-view 3D reconstruction
Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin
Choose Settings Carefully: Comparing Action Unit detection at Different Settings Using a Large-Scale Dataset
Mina Bishay, Ahmed Ghoneim, Mohamed Ashraf, Mohammad Mavadati
Which CNNs and Training Settings to Choose for Action Unit Detection? A Study Based on a Large-Scale Dataset
Mina Bishay, Ahmed Ghoneim, Mohamed Ashraf, Mohammad Mavadati