Attentive Linear Transformation for Image Captioning

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Senmao YeJunwei Han

Abstract

We propose a novel attention framework called attentive linear transformation (ALT). Instead of learning the spatial or channel-wise attention in existing models, ALT learns to attend to the high-dimensional transformation matrix from the image feature space to the context vector space. Thus ALT can learn various relevant feature abstractions, including spatial attention, channel-wise attention and visual dependence. Besides, we propose a soft threshold regression to predict the attention probabilities for local regions. Soft threshold regression preserves more useful visual information than popular softmax regression. Extensive experiments on the MS COCO and the Flickr30k datasets demonstrate the superiority of our model compared with other state-of-the-art methods.

References

Aug 1, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Girish KulkarniTamara L Berg
Jun 9, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Haoran XieYanghui Rao
Dec 4, 2016·IEEE Transactions on Neural Networks and Learning Systems·Nian LiuXuelong Li
Jan 13, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Junwei HanFeiping Nie
Apr 12, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Nian Liu, Junwei Han

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Citations

Jul 1, 2020·Journal of X-ray Science and Technology·Sivamurugan Vellakani, Indumathi Pushbam

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