HVLM: Exploring Human-Like Visual Cognition and Language-Memory Network for Visual Dialog

Abstract

Visual dialog, a visual-language task, enables an AI agent to engage in conversation with humans grounded in a given image. To generate appropriate answers for a series of questions in the dialog, the agent is required to understand the comprehensive visual content of an image and the fine-grained textual context of the dialog. However, previous studies typically utilized the object-level visual feature to represent a whole image, which only focuses on the local perspective of an image but ignores the importance of the global information in an image. In this paper, we proposed a novel model Human-Like Visual Cognitive and Language-Memory Network for Visual Dialog (HVLM), to simulate global and local dual-perspective cognitions in the human visual system and understand an image comprehensively. HVLM consists of two key modules, Local-to-Global Graph Convolutional Visual Cognition (LG-GCVC) and Question-guided Language Topic Memory (T-Mem). Specifically, in the LG-GCVC module, we design a question-guided dual-perspective reasoning to jointly learn visual contents from both local and global perspectives through a simple spectral graph convolution network. Furthermore, in the T-Mem module, we design an iterative learning strategy to gradually enhance fine-grained textual context details via an attention mechanism. Experimental results demonstrate the superiority of our proposed model, which obtains the comparable performance on benchmark datasets VisDial v1.0 and VisDial v0.9.