Multi-objective Layer-wise Optimization and Multi-level Probability Fusion for Image Description Generation Using LSTM
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摘要: 使用计算模型对图像进行自动描述属于视觉高层理解，要求模型不仅能够对图像中的目标及场景进行描述，而且能够对目标与目标之间、目标与场景之间的关系进行表达，同时能够生成符合一定语法和结构的自然语言句子.目前基于深度卷积神经网络（Convolutional neural network，CNN）和长短时记忆网络（Long-short term memory，LSTM）的方法已成为解决该问题的主流，虽然已取得巨大进展，但存在LSTM层次不深，难以优化的问题，导致模型性能难以提升，生成的描述句子质量不高.针对这一问题，受深度学习思想的启发，本文设计了基于逐层优化的多目标优化及多层概率融合的LSTM（Multi-objective layer-wise optimization/multi-layer probability fusion LSTM，MLO/MLPF-LSTM）模型.模型中首先使用浅层LSTM进行训练，收敛之后，保留原LSTM模型中的分类层及目标函数，并添加新的LSTM层及目标函数重新对模型进行训练，对模型原有参数进行微调；在测试时，将多个分类层使用Softmax函数进行变换，得到每层对单词的预测概率分值，然后将多层的概率分值进行加权融合，得到单词的最终预测概率.在MSCOCO和Flickr30K两个数据集上实验结果显示，该模型性能显著，在多个统计指标上均超过了同类其他方法.Abstract: The task of image automatic description by computer belongs to high-level visual understanding. Unlike image classification, object detection, etc., it usually requires that the model should not only have abilities of describing scene and objects, but also have capacities of expressing the relations between different objects and between objects and background in the image. In addition, it is required that the model should generate natural sentences which accord with correct grammars and appropriate structures. Nowadays, the approaches based on convolutional neural network (CNN) and long-short term memory network (LSTM) have been the popular solutions to this task, and a series of successes have been obtained. However, there are still several sticky problems, for instance, the LSTM network is not deep enough and the model is difficult to optimize, and as a result, performances cannot be improved and the sentences generated are of low quantity. To address these difficulties, inspired by the idea of deep learning, a model named MLO/MLPF-LSTM is proposed, in which the method of layer-wise optimization, multi-objective optimization and multi-layer probability fusion are employed. In details, an LSTM network with shallow depth is trained firstly, then, new LSTM layers and related objective functions are added to the optimized LSTM network. Meanwhile, the classification layers and objective functions in the original LSTM model are reserved and fine-tuned with the new layers. During the test, the probabilities of all the Softmax functions which are fed with the corresponding classification layers are fused for the final predicted probabilities by a weighted average method. Experimental results on MSCOCO and Flickr30K datasets demonstrate that our model is effective and outperforms other methods of same kinds on a number of evaluation metrics.1) 本文责任编委 王立威
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图 4 MLPF-LSTM图像描述生成流程
Fig. 4 The pipeline of image description generation in MLPF-LSTM
图 6 MLO/MLPF-LSTM (3-stage)模型生成的部分图像描述示例
Fig. 6 Examples of image descriptions with MLO/ MLPF-LSTM (3-stage)
图 7 在MSCOCO数据集上使用不同策略加深模型深度时的性能表现
Fig. 7 Performance under different strategies at each stage on MSCOCO
表 1 MSCOCO数据集上不同层次及多层融合之后的性能对比(非联调方式) ($\%$)
Table 1 Performance comparison under different fusion conditions on MSCOCO (non-jointly optimizing) ($\%$)
Models B-1 B-2 B-3 B-4 C Baseline 67.7 49.4 35.2 25.0 78.2 2-stage P1 67.8 49.7 35.3 25.0 78.5 P2 67.5 49.6 35.3 25.0 79.6 Fusion 68.0 50.0 35.5 25.1 79.1 3-stage P1 67.9 49.8 35.5 25.2 79.0 P2 67.5 49.6 35.3 25.0 79.6 P3 67.3 49.4 35.1 24.8 78.9 Fusion 68.0 50.0 35.8 25.4 80.2 4-stage P1 67.6 49.5 35.3 25.1 78.7 P2 67.0 49.1 34.9 24.8 79.7 P3 66.8 49.0 34.8 24.7 79.5 P4 66.9 49.0 34.8 24.6 78.9 Fusion 67.7 49.8 35.6 25.3 80.4 C表示CIDEr 
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表 2 MSCOCO数据集上不同层次及多层融合之后的性能对比(联调方式) ($\%$)
Table 2 Performance comparison under different fusion conditions on MSCOCO (jointly optimizing) ($\%$)
Models B-1 B-2 B-3 B-4 C Baseline$^+$ 70.2 52.7 38.3 27.6 86.2 2-stage P1 70.2 52.7 38.4 27.8 88.4 P2 69.9 52.6 38.3 27.7 87.5 Fusion 70.2 52.8 38.4 27.8 88.5 3-stage P1 70.5 52.8 38.4 27.8 89.3 P2 70.1 52.5 38.2 27.8 88.9 P3 70.1 52.8 38.5 27.9 88.2 Fusion 70.6 53.2 38.8 28.2 90.0 C表示CIDEr
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表 3 Flickr30K数据集上不同层次及多层融合之后的性能对比(联调方式) ($\%$)
Table 3 Performance comparison under different fusion conditions on Flickr30K (jointly optimizing) ($\%$)
Models B-1 B-2 B-3 B-4 M Baseline$^+$ 60.2 41.8 28.5 19.2 19.2 2-stage P1 61.5 42.9 29.2 19.7 19.4 P2 60.7 42.2 29.0 19.8 19.2 Fusion 61.4 42.8 29.2 19.8 19.6 M表示METEOR
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表 4 MSCOCO数据集上不同层次及多层融合之后的性能对比(使用联调方式和集束搜索算法) ($\%$)
Table 4 Performance comparison under different fusion conditions on MSCOCO (jointly optimizing and Beam search algorithm are employed) ($\%$)
Models B-1 B-2 B-3 B-4 C Baseline$^+$ 71.3 54.4 40.8 30.5 92.0 2-stage P1 71.4 54.3 40.7 30.6 93.8 P2 71.6 54.8 41.1 31.0 93.7 Fusion 71.5 54.5 41.0 31.0 94.2 C表示CIDEr
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表 5 Flickr30K数据集上不同层次及多层融合之后的性能对比(使用联调方式和集束搜索算法) ($\%$)
Table 5 Performance comparison under different fusion conditions on Flickr30K (jointly optimizing and Beam search algorithm are employed) ($\%$)
Models B-1 B-2 B-3 B-4 M Baseline$^+$ 63.4 44.5 30.9 21.1 19.0 2-stage P1 65.1 45.8 31.8 21.9 19.2 P2 65.0 46.0 32.0 21.9 19.3 Fusion 66.2 47.2 33.1 23.0 19.6 M表示METEOR
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表 6 不同方法在MSCOCO数据集上的性能对比($\%$)
Table 6 Performance comparison with other state-of-the-art methods on MSCOCO ($\%$)
Methods B-1 B-2 B-3 B-4 C multimodal RNN[14] 62.5 45.0 32.1 23.0 66.0 Google NIC[2] 66.6 46.1 32.9 24.6 -- LRCN-AlexNet[13] 62.8 44.2 30.4 21.0 -- m-RNN[1] 67.0 49.0 35.0 25.0 -- Soft-attention[15] 70.7 49.2 34.4 24.3 -- Hard-attention[15] 71.8 50.4 35.7 25.0 -- emb-gLSTM, Gaussian[28] 67.0 49.1 35.8 26.4 81.3 MLO/MLPF-LSTM 67.7 49.8 35.6 25.3 80.4 MLO/MLPF-LSTM$^+$ 70.6 53.2 38.8 28.2 90.0 MLO/MLPF-LSTM$^+$(BS) 71.5 54.5 41.0 31.0 94.2 BS表示Beam search, C表示CIDEr
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表 7 不同方法在Flickr30K数据集上的性能对比($\%$)
Table 7 Performances comparison with other state-of-the-art methods on Flickr30K ($\%$)
Methods B-1 B-2 B-3 B-4 M multimodal RNN[14] 57.3 36.9 24.0 15.7 15.3 Google NIC[2] 66.3 42.3 27.7 18.3 -- LRCN-AlexNet[13] 58.7 39.1 25.1 16.5 -- m-RNN[1] 60.0 41.0 28.0 19.0 -- Soft-attention[15] 66.7 43.4 28.8 19.1 18.5 Hard-attention[15] 66.9 43.9 29.6 19.9 18.5 emb-gLSTM, Gaussian[28] 64.6 44.6 30.5 20.6 17.9 MLO/MLPF-LSTM$^+$ 61.4 42.8 29.2 19.8 19.6 MLO/MLPF-LSTM$^+$(BS) 66.2 47.2 33.1 23.0 19.6 M表示METEOR, BS表示Beam search
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