ADC–CF: Adaptive deep concatenation coder framework for visual question answering

Abstract

Multimodal teaching activity faces significant problems in Visual Question Answering (VQA), which involves simultaneous comprehension with reduced performance fidelity. However, Conventional methods are employed for portrayal and queries in a defined manner, which fails to accomplish the required performance accuracy rate. For elucidating the excellent image and question representation, this paper suggests an Adaptive Deep Concatenated Coder Framework (ADC–CF) that enrolls both the image and question attributes simultaneously with the optimized residual layer. The Coder Framework comprises of cascaded layers of Encoder-Decoder architecture, which captures rich, meaningful query characteristics and image details through the use of keywords employing significant object areas in the picture. ADC–CF layer has an encoder segment that blueprints the self-recognition of queries in which questions are concatenated to limit the answers and decoder segment blueprints the commanded-recognition of images. The simulation results of ADC–CF are tested with both the VQA datasets 1.0 and 2.0 and manifests an improved performance accuracy ratio of 72.45% for 1.0 dataset and 73.57% for 2.0 datasets, thus proving the reliability of the proposed framework.