Guiding Long-Short Term Memory for Image Caption Generation
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Abstract
In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search in order to prevent from favoring short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or even outperform the current state-of-the-art.Keywords
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This publication has 14 references indexed in Scilit:
- Long-Term Recurrent Convolutional Networks for Visual Recognition and DescriptionIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Deep Visual-Semantic Alignments for Generating Image DescriptionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- LSTM: A Search Space OdysseyIEEE Transactions on Neural Networks and Learning Systems, 2016
- Going deeper with convolutions2015
- Show and tell: A neural image caption generator2015
- From captions to visual concepts and back2015
- From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptionsTransactions of the Association for Computational Linguistics, 2014
- Microsoft COCO: Common Objects in Context2014
- BabyTalk: Understanding and Generating Simple Image DescriptionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- Every Picture Tells a Story: Generating Sentences from Images2010