부산대학교 도서관

Creating descriptive phrases with visual and textual data is known as image captioning. Transformers use an encoder and decoder configuration to manage language comprehension and machine translation. As part of our effort to create a small and lightweight model that can be deployed with ease, we present the Lightweight Transformer with an embedded GRU decoder for image captioning. We reduce the usual architecture in this model by reducing the number of encoders and decoders to only one encoder and a GRU-integrated decoder. Furthermore, including multilevel rich visual features from Inception V3 enhances the encoder's performance. We conducted a number of thorough experiments to assess the effectiveness of this suggested Lightweight Transformer architecture using the Viz Wiz Captions dataset.