FAN: Focused Attention Networks
This work addresses the challenge of enhancing attention mechanisms for better relation modeling in AI, though it appears incremental as it builds on existing attention frameworks.
The paper tackles the problem of learning attention weights in networks by introducing a center-mass cross entropy loss and focused attention backbone to better emphasize informative pair-wise relations between entities, resulting in state-of-the-art performance in relationship proposal and improved results across vision and language tasks.
Attention networks show promise for both vision and language tasks, by emphasizing relationships between constituent elements through weighting functions. Such elements could be regions in an image output by a region proposal network, or words in a sentence, represented by word embedding. Thus far the learning of attention weights has been driven solely by the minimization of task specific loss functions. We introduce a method for learning attention weights to better emphasize informative pair-wise relations between entities. The key component is a novel center-mass cross entropy loss, which can be applied in conjunction with the task specific ones. We further introduce a focused attention backbone to learn these attention weights for general tasks. We demonstrate that the focused supervision leads to improved attention distribution across meaningful entities, and that it enhances the representation by aggregating features from them. Our focused attention module leads to state-of-the-art recovery of relations in a relationship proposal task and boosts performance for various vision and language tasks.