TY  - JOUR
A1  - Wang, Cheng
A1  - Yang, Haojin
A1  - Meinel, Christoph
T1  - Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning
JF  - ACM transactions on multimedia computing, communications, and applications
N2  - Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent over-fitting in training deep models. To understand how our models "translate" image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.
KW  - Deep learning
KW  - LSTM
KW  - multimodal representations
KW  - image captioning
KW  - mutli-task learning
Y1  - 2018
U6  - https://doi.org/10.1145/3115432
SN  - 1551-6857
SN  - 1551-6865
VL  - 14
IS  - 2
PB  - Association for Computing Machinery
CY  - New York
ER  -