Aim 2
AIM (presumably an acronym for a research initiative or challenge) focuses on advancing various computer vision and machine learning tasks, primarily through efficient model design and improved data utilization. Current research emphasizes developing real-time, resource-efficient solutions for video super-resolution, depth map processing, and image quality assessment, often leveraging neural networks and techniques like knowledge distillation and low-precision inference. These advancements have significant implications for applications ranging from augmented and virtual reality to mobile photography and video streaming, improving both the quality and accessibility of these technologies.
Papers
Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results
Marcos V. Conde, Florin-Alexandru Vasluianu, Jinhui Xiong, Wei Ye, Rakesh Ranjan, Radu Timofte
AIM 2024 Challenge on UHD Blind Photo Quality Assessment
Vlad Hosu, Marcos V. Conde, Lorenzo Agnolucci, Nabajeet Barman, Saman Zadtootaghaj, Radu Timofte
AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark
Michal Nazarczuk, Thomas Tanay, Sibi Catley-Chandar, Richard Shaw, Radu Timofte, Eduardo Pérez-Pellitero
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin, Radu Timofte, Gen Zhan, Li Yang, Yunlong Tang, Yiting Liao, Jiongzhi Lin, Baitao Huang, Morteza Moradi, Mohammad Moradi, Francesco Rundo, Concetto Spampinato, Ali Borji, Simone Palazzo, Yuxin Zhu, Yinan Sun, Huiyu Duan, Yuqin Cao, Ziheng Jia, Qiang Hu, Xiongkuo Min, Guangtao Zhai, Hao Fang, Runmin Cong, Xiankai Lu, Xiaofei Zhou, Wei Zhang, Chunyu Zhao, Wentao Mu, Tao Deng, Hamed R. Tavakoli