We present Language-binding Object Graph Network, the first neural reasoning
method with dynamic relational structures across both visual and textual
domains with applications in visual question answering. Relaxing the common
assumption made by current models that the object predicates pre-exist and stay
static, passive to the reasoning process, we propose that these dynamic
predicates expand across the domain borders to include pair-wise
visual-linguistic object binding. In our method, these contextualized object
links are actively found within each recurrent reasoning step without relying
on external predicative priors. These dynamic structures reflect the
conditional dual-domain object dependency given the evolving context of the
reasoning through co-attention. Such discovered dynamic graphs facilitate
multi-step knowledge combination and refinements that iteratively deduce the
compact representation of the final answer. The effectiveness of this model is
demonstrated on image question answering demonstrating favorable performance on
major VQA datasets. Our method outperforms other methods in sophisticated
question-answering tasks wherein multiple object relations are involved. The
graph structure effectively assists the progress of training, and therefore the
network learns efficiently compared to other reasoning models.

本文提出了一种首个跨越视觉和文本领域的动态关系结构的神经推理方法，可在视觉问答中应用，采用其动态结构反映不断演变的推理上下文，发现有助于多步知识组合和优化的动态图优化问题求解能力。