Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Logically Consistent Loss for Visual Question Answering   Given an image, a back-ground knowledge, and a set of questions about an
object, human learners answer the questions very consistently regardless of
question forms and semantic tasks. The current advancement in neural-network
based Visual Question Answering (VQA), despite their impressive performance,
cannot ensure such consistency due to identically distribution (i.i.d.)
assumption. We propose a new model-agnostic logic constraint to tackle this
issue by formulating a logically consistent loss in the multi-task learning
framework as well as a data organisation called family-batch and hybrid-batch.
To demonstrate usefulness of this proposal, we train and evaluate MAC-net based
VQA machines with and without the proposed logically consistent loss and the
proposed data organization. The experiments confirm that the proposed loss
formulae and introduction of hybrid-batch leads to more consistency as well as
better performance. Though the proposed approach is tested with MAC-net, it can
be utilised in any other QA methods whenever the logical consistency between
answers exist.

11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg