Attention Mechanism-Based Cognition-Level Scene Understanding

Given a question–image input, a visual commonsense reasoning (VCR) model predicts an answer with a corresponding rationale, which requires inference abilities based on real-world knowledge. The VCR task, which calls for exploiting multi-source information as well as learning different levels of understanding and extensive commonsense knowledge, is a cognition-level scene understanding challenge. The VCR task has aroused researchers’ interests 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 have generally relied on pre-training or exploiting memory with long-term dependency relationship-encoded models. However, these approaches suffer from a lack of generalizability and a loss of information in long sequences. In this work, we propose a parallel attention-based cognitive VCR network, termed 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 an intuitive interpretation of visual commonsense reasoning.