.png){kind=icon}
.png){kind=icon}
Return
Stimulus-driven and concept-driven analysis for image caption generation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2019.04.095.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, image captioning has achieved great progress in computer vision and artificial intelligence. However, language models still failed to achieve the desired results in high-level visual tasks. Generating accurate image captions for a complex scene that contains multiple targets is a challenge. To solve these problems, we introduce the theory of attention in psychology to image caption generation. We propose two types of attention mechanisms: The stimulus-driven and the concept-driven. Our attention model relies on a combination of convolutional neural network (CNN) over images and long-short term memory (LSTM) network over sentences. Comparison of experimental results illustrates that our proposed method achieves good performance on the MSCOCO test server. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Image captioning
Stimulus-driven
Concept-driven
Attention mechanism
LSTM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W