BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling. (May 2021) - Su, Jing; Dai, Qingyun; Guerin, Frank; Zhou, Mian
Highlights: We propose a novel end-to-end BERT-dcLSTM framework to automatically generate coherent descriptions for sequential images. Firstly, we have exploited a pre-training (BERT) model to obtain sentence representation and word representation of the dataset which can efficiently enrich the meaning of sentences. Furthermore, a dual chain LSTM (dcLSTM) has been used to learn the relations between sequential images and corresponding descriptions and generate more coherent descriptions for sequential images. We have evaluated the performance of our proposed approach on the Sequential Image Narrative Dataset (SIND). Experimental results show that our proposed model outperforms the other closely related baselines under the automatic metrics BLEU and CIDEr, and can generate more consistent descriptions and efficiently learn the dependencies between images. Abstract: Visual storytelling is a creative and challenging task, aiming to automatically generate a story-like description for a sequence of images. The descriptions generated by previous visual storytelling approaches lack coherence because they use word-level sequence generation methods and do not adequately consider sentence-level dependencies. To tackle this problem, we propose a novel hierarchical visual storytelling framework which separately models sentence-level and word-level semantics. We use the transformer-based BERT to obtain embeddings for sentences and words. We then employ a hierarchical LSTM network: the bottom LSTM receives as input the sentence vector representation from BERT, to learn the dependencies between the sentences corresponding to images, and the top LSTM is responsible for generating the corresponding word vector representations, taking input from the bottom LSTM. Experimental results demonstrate that our model outperforms most closely related baselines under automatic evaluation metrics BLEU and CIDEr, and also show the effectiveness of our method with human evaluation.