Enriching image descriptions by fusing fine-grained semantic features with a transformer

WANG Junhao; LUO Yifeng

doi:10.3969/j.issn.1000-5641.202091004

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Sep. 2020

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WANG Junhao, LUO Yifeng. Enriching image descriptions by fusing fine-grained semantic features with a transformer[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 56-67. doi: 10.3969/j.issn.1000-5641.202091004

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WANG Junhao, LUO Yifeng. Enriching image descriptions by fusing fine-grained semantic features with a transformer[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 56-67. doi: 10.3969/j.issn.1000-5641.202091004

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Enriching image descriptions by fusing fine-grained semantic features with a transformer

doi: 10.3969/j.issn.1000-5641.202091004

WANG Junhao,
LUO Yifeng^,

School of Data Science and Engineering, East China Normal University, Shanghai　200062, China

Received Date: 2020-08-04
Available Online: 2020-09-24
Publish Date: 2020-09-24

Abstract

Abstract

Modern image captioning models following the encoder-decoder architecture of a convolutional neural network (CNN) or recurrent neural network (RNN) face the issue of dismissing a large amount of detailed information contained in images and the high cost of training time. In this paper, we propose a novel model, consisting of a compact bilinear encoder and a compact multi-modal decoder, to improve image captioning with fine-grained regional object features. In the encoder, compact bilinear pooling (CBP) is used to encode fine-grained semantic features from an image’s regional features and transformers are used to encode global semantic features from an image’s global bottom-up features; the collective encoded features are subsequently fused using a gate structure to form the overall encoded features of the image. In the decoding process, we extract multi-modal features from fine grained regional object features, and fuse them with overall encoded features to decode semantic information for description generation. Extensive experiments performed on the public Microsoft COCO dataset show that our model achieves state-of-the-art image captioning performance.
- image captioning,
- fine-grained features,
- multi-modal features,
- transformer

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References(40)

References

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