Weakly Supervised Learning for Real-World Computer Vision Applications

Weakly supervised learning refers to a variety of studies that attempt to address the challenging pattern recognition tasks by learning from weak or imperfect supervision. Supervised learning methods including Deep Convolutional Neural Networks (DCNNs) have significantly improved the performance in many problems in the field of computer vision, thanks to the rise of large-scale annotated data set and the advance in computing hardware. However, these supervised learning approaches are notorious "data hungry", which makes them are sometimes not practical in many real-world industrial applications. We are often facing the problem that we are not able to acquire enough amount of perfect annotations (e.g., object bounding boxes and pixel-wise masks) for reliable training models. To address this problem, many efforts in so-called weakly supervised learning approaches have been made to improve the DCNNs training to deviate from traditional paths of supervised learning using imperfect data. For instance, various approaches have proposed new loss functions or novel training schemes. Weakly supervised learning is a popular research direction in Computer Vision and Machine Learning communities, many research works have been devoted to related topics, leading to rapid growth of related publications in the top-tier conferences and journals such as CVPR, ICCV, ECCV, T-IP, and T-PAMI. We organize this workshop to investigate current ways of building industry level AI system relying on learning from imperfect data. We hope this workshop will attract attention and discussions from both industry and academic people.