Multimodal Interaction Network for Image Captioning
In image captioning,multimodal approaches are widely exploited by simultaneously providing visual inputs and semantic attributes to capture multi-level information.However,most approaches still utilize the two modalities in isolation,without considering the correlation between them.With the aim of filling this gap,we first introduce a Bi-Directional Attention Flow(BiDAF)module that extends the self attention mechanism as a bi-directional manner to model complex interactions between different modalities.Then,through a Gated Linear Memory(GLM)module that can realize the same function as a Long Short-Term Memory(LSTM)with only one forget gate,the decoder complexity is effectively reduced and multi-modal interaction information is captured.Finally,we apply BiDAF and GLM as the encoder and the decoder of the image captioning model respectively,forming a Multimodal Interactive Network(MINet).When tested on COCO,experimental results show that MINet not only has a more concise decoder,better image description,and higher evaluation scores than that of existing multimodal methods,but also more efficient in image description without pre-training.
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