Active learning is an effective way to relieve the tedious work of manual annotation in many applications of visual recognition. The vast majority of previous works, if not all of them, focused on active learning with a single human oracle. The problem of active learning with multiple oracles in a collaborative setting has not been well explored. Moreover, most of the previous works assume that the labels provided by the human oracles are noise free, which may often be violated in reality.
To solve the above-mentioned issues, we proposed two models (i.e., a distributed multi-labeler active learning model and a centralized multi-labeler active learning model) for collaborative active visual recognition from the crowds, where we explore how we can effectively model the labelers’ expertise in a crowdsourcing labeling system to build better visual recognition models. Both two models are not only robust to label noises, but also a principled label quality measure to online detect irresponsible labelers. We also extended the centralized multi-labeler active learning model from binary cases to multi-class cases and also incorporate the idea of reinforcement learning to actively select both the informative samples and the high-quality annotators, which better explores the trade-off between exploitation and exploration. Our collaborative active learning models have been validated in the real-world visual recognition benchmark datasets. The experimental results strongly show the validity and efficiency of the two proposed models.
In this talk, I also would like to share my deep learning experience as a direct extension of our data collection efforts via collaborative active learning from crowds.
Chengjiang Long received his Ph.D. degree from Stevens Institute of Technology in 2015. Currently, he is a computer vision researcher in the computer vision team at Kitware, a leader in the creation and support of state-of-the-art technology, providing robust solutions to academic and government institutions, such as DARPA, IARPA, and the Army, as well as private corporations worldwide. Prior to joining Kitware, he ever worked at NEC Labs America and GE
Global Research in 2013 and 2015, respectively. To date, he has published more than 20 papers in reputed international journals and conferences, and has been serving as a reviewer for top international journals (e.g., TIP, MM, MVAP and TVCJ) and conferences (e.g., CVPR,ICCV, ECCV, ACCV, BMVC and ICME). For more information, please refer to his website:http://www.chengjianglong.com/.