  Visual Question and Answering (VQA) problems are attracting increasing
interest from multiple research disciplines. Solving VQA problems requires
techniques from both computer vision for understanding the visual contents of a
presented image or video, as well as the ones from natural language processing
for understanding semantics of the question and generating the answers.
Regarding visual content modeling, most of existing VQA methods adopt the
strategy of extracting global features from the image or video, which
inevitably fails in capturing fine-grained information such as spatial
configuration of multiple objects. Extracting features from auto-generated
regions -- as some region-based image recognition methods do -- cannot
essentially address this problem and may introduce some overwhelming irrelevant
features with the question. In this work, we propose a novel Focused Dynamic
Attention (FDA) model to provide better aligned image content representation
with proposed questions. Being aware of the key words in the question, FDA
employs off-the-shelf object detector to identify important regions and fuse
the information from the regions and global features via an LSTM unit. Such
question-driven representations are then combined with question representation
and fed into a reasoning unit for generating the answers. Extensive evaluation
on a large-scale benchmark dataset, VQA, clearly demonstrate the superior
performance of FDA over well-established baselines.
