  In this paper a new formulation of event recognition task is examined: it is
required to predict event categories in a gallery of images, for which albums
(groups of photos corresponding to a single event) are unknown. We propose the
novel two-stage approach. At first, features are extracted in each photo using
the pre-trained convolutional neural network. These features are classified
individually. The scores of the classifier are used to group sequential photos
into several clusters. Finally, the features of photos in each group are
aggregated into a single descriptor using neural attention mechanism. This
algorithm is optionally extended to improve the accuracy for classification of
each image in an album. In contrast to conventional fine-tuning of
convolutional neural networks (CNN) we proposed to use image captioning, i.e.,
generative model that converts images to textual descriptions. They are one-hot
encoded and summarized into sparse feature vector suitable for learning of
arbitrary classifier. Experimental study with Photo Event Collection and
Multi-Label Curation of Flickr Events Dataset demonstrates that our approach is
9-20% more accurate than event recognition on single photos. Moreover, proposed
method has 13-16% lower error rate than classification of groups of photos
obtained with hierarchical clustering. It is experimentally shown that the
image captions trained on Conceptual Captions dataset can be classified more
accurately than the features from object detector, though they both are
obviously not as rich as the CNN-based features. However, it is possible to
combine our approach with conventional CNNs in an ensemble to provide the
state-of-the-art results for several event datasets.
