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Truncation Cross Entropy Loss for Remote Sensing Image Captioning

机译：遥感图像标题的截断交叉熵损耗

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摘要

Recently, remote sensing image captioning (RSIC) has drawn an increasing attention. In this field, the encoder-decoder-based methods have become the mainstream due to their excellent performance. In the encoder-decoder framework, the convolutional neural network (CNN) is used to encode a remote sensing image into a semantic feature vector, and a sequence model such as long short-term memory (LSTM) is subsequently adopted to generate a content-related caption based on the feature vector. During the traditional training stage, the probability of the target word at each time step is forcibly optimized to 1 by the cross entropy (CE) loss. However, because of the variability and ambiguity of possible image captions, the target word could be replaced by other words like its synonyms, and therefore, such an optimization strategy would result in the overfitting of the network. In this article, we explore the overfitting phenomenon in the RSIC caused by CE loss and correspondingly propose a new truncation cross entropy (TCE) loss, aiming to alleviate the overfitting problem. In order to verify the effectiveness of the proposed approach, extensive comparison experiments are performed on three public RSIC data sets, including UCM-captions, Sydney-captions, and RSICD. The state-of-the-art result of Sydney-captions and RSICD and the competitive results of UCM-captions achieved by TCE loss demonstrate that the proposed method is beneficial to RSIC.
机译：最近，遥感图像标题（RSIC）引起了越来越多的关注。在此领域，由于其出色的性能，基于编码器 - 解码器的方法已成为主流。在编码器 - 解码器框架中，卷积神经网络（CNN）用于将遥感图像编码为语义特征向量，随后采用诸如长短期存储器（LSTM）的序列模型来生成内容 - 基于特征向量的相关标题。在传统训练阶段，通过跨熵（CE）损耗强制优化目标字的概率。然而，由于可能的图像标题的变异性和歧义，目标词可以被其同义词的其他单词替换，因此，这样的优化策略将导致网络的过度。在本文中，我们探讨了CE亏损引起的RSIC中的过度拟合现象，并相应地提出了一种新的截断交叉熵（TCE）损失，旨在缓解过度拟合问题。为了验证所提出的方法的有效性，在三个公共RSIC数据集中进行了广泛的比较实验，包括UCM字幕，悉尼标题和RSICD。悉尼标题和RSICD的最先进结果以及通过TCE损失所实现的UCM标题的竞争结果表明，所提出的方法有利于RSIC。

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