Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Modular Co-Attention Networks for Visual Question Answering   Visual Question Answering (VQA) requires a fine-grained and simultaneous
understanding of both the visual content of images and the textual content of
questions. Therefore, designing an effective `co-attention' model to associate
key words in questions with key objects in images is central to VQA
performance. So far, most successful attempts at co-attention learning have
been achieved by using shallow models, and deep co-attention models show little
improvement over their shallow counterparts. In this paper, we propose a deep
Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA)
layers cascaded in depth. Each MCA layer models the self-attention of questions
and images, as well as the guided-attention of images jointly using a modular
composition of two basic attention units. We quantitatively and qualitatively
evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation
studies to explore the reasons behind MCAN's effectiveness. Experimental
results demonstrate that MCAN significantly outperforms the previous
state-of-the-art. Our best single model delivers 70.63$\%$ overall accuracy on
the test-dev set. Code is available at https://github.com/MILVLG/mcan-vqa.

30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th