  In this paper we propose an end-to-end trainable deep neural network model
for egocentric activity recognition. Our model is built on the observation that
egocentric activities are highly characterized by the objects and their
locations in the video. Based on this, we develop a spatial attention mechanism
that enables the network to attend to regions containing objects that are
correlated with the activity under consideration. We learn highly specialized
attention maps for each frame using class-specific activations from a CNN
pre-trained for generic image recognition, and use them for spatio-temporal
encoding of the video with a convolutional LSTM. Our model is trained in a
weakly supervised setting using raw video-level activity-class labels.
Nonetheless, on standard egocentric activity benchmarks our model surpasses by
up to +6% points recognition accuracy the currently best performing method that
leverages hand segmentation and object location strong supervision for
training. We visually analyze attention maps generated by the network,
revealing that the network successfully identifies the relevant objects present
in the video frames which may explain the strong recognition performance. We
also discuss an extensive ablation analysis regarding the design choices.
