  Given a question-image input, the Visual Commonsense Reasoning (VCR) model
can predict an answer with the corresponding rationale, which requires
inference ability from the real world. The VCR task, which calls for exploiting
the multi-source information as well as learning different levels of
understanding and extensive commonsense knowledge, is a cognition-level scene
understanding task. The VCR task has aroused researchers' interest due to its
wide range of applications, including visual question answering, automated
vehicle systems, and clinical decision support. Previous approaches to solving
the VCR task generally rely on pre-training or exploiting memory with long
dependency relationship encoded models. However, these approaches suffer from a
lack of generalizability and losing information in long sequences. In this
paper, we propose a parallel attention-based cognitive VCR network PAVCR, which
fuses visual-textual information efficiently and encodes semantic information
in parallel to enable the model to capture rich information for cognition-level
inference. Extensive experiments show that the proposed model yields
significant improvements over existing methods on the benchmark VCR dataset.
Moreover, the proposed model provides intuitive interpretation into visual
commonsense reasoning.
