Adaptive Pooling in Multi-Instance Learning for Web Video Annotation

Web videos are usually weakly annotated, i.e., a tag is associated to a video once the corresponding concept appears in a frame of this video without indicating when and where it occurs. These weakly annotated tags pose big troubles to many Web video applications, e.g. search and recommendation. In this paper, we present a new Web video annotation approach based on multi-instance learning (MIL) with a learnable pooling function. By formulating the Web video annotation as a MIL problem, we present an end-to-end deep network framework to solve this problem in which the frame (instance) level annotation is estimated from tags given at the video (bag of instances) level via a convolutional neural network (CNN). A learnable pooling function is proposed to adaptively fuse the outputs of the CNN to determine tags at the video level. We further propose a new loss function that consists of both bag-level and instance-level losses, which enables the penalty term to be aware of the internal state of network rather than only an overall loss, thus makes the pooling function learned better and faster. Experimental results demonstrate that our proposed framework is able to not only enhance the accuracy of Web video annotation by outperforming the state-of-the-art Web video annotation methods on the large-scale video dataset FCVID, but also help to infer the most relevant frames in Web videos.