Figure Captioning with Reasoning and Sequence-Level Training

06/07/2019 ∙ by Charles Chen, et al. ∙ Ohio University 0

Figures, such as bar charts, pie charts, and line plots, are widely used to convey important information in a concise format. They are usually human-friendly but difficult for computers to process automatically. In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure. While natural image captioning has been studied extensively, figure captioning has received relatively little attention and remains a challenging problem. First, we introduce a new dataset for figure captioning, FigCAP, based on FigureQA. Second, we propose two novel attention mechanisms. To achieve accurate generation of labels in figures, we propose Label Maps Attention. To model the relations between figure labels, we propose Relation Maps Attention. Third, we use sequence-level training with reinforcement learning in order to directly optimizes evaluation metrics, which alleviates the exposure bias issue and further improves the models in generating long captions. Extensive experiments show that the proposed method outperforms the baselines, thus demonstrating a significant potential for the automatic captioning of vast repositories of figures.

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1 Introduction

Understanding images has been an important area of investigation within computer vision and natural language processing. Recent work has shown excellent performance on a number of tasks, especially image captioning and Visual Question Answering (VQA). Figures, as a specific type of images, convey useful information, such as trends, proportions and values, in a concise format. People can understand these attributes at a glance. Therefore, people usually use figures (e.g., bar chart, pie chart, and line plot) in documents, reports and talks to efficiently communicate ideas. Figure captioning aims at generating a natural language description of a figure, by inferring potential logical relations between elements in the figure. This topic is interesting from the artificial intelligence perspective: the machines would extract the relations between the labels in the figures based on visual intuitions, instead of reconstructing the source data,

i.e., inverting the visualization pipeline.

While natural image captioning has been extensively studied in computer vision, figure captioning has received little attention. Depending on the user case, the generated caption may be a high-level description of the figure, or it may include more details such as relations among the data presented in the figure. There are two major challenges in this task. First, it requires an understanding of the labels and relations among labels in a figure. Second, the figure captions typically contains a few sentences, which are usually longer than the captions for natural images (e.g. MSCOCO dataset Lin et al. (2014)). As a long-text-generation task, figure captioning will accumulate more errors as more words predicted.

A similar problem of understanding figures is VQA. However, figure captioning distinguishes itself from VQA in two important aspects. First, the input is different. The input to a VQA system consists of an image/figure to be queried and a question. A figure captioning system automatically generates a description of the figure, which can be regarded as a self-asking VQA task. Second, the output of a VQA system is the answer to the given question, commonly containing only a few words. In contrast, a figure captioning system usually produces a few sentences.

In this paper, we investigate the problem of figure captioning. Our main contributions in this work are:

  • We introduce a new dataset for figure captioning called FigCAP.

  • We propose two novel attention mechanisms to improve the decoder’s performance. The Label Maps Attention enables the decoder to focus on specific labels. The Relation Maps Attention is proposed to discover the relations between figure labels.

  • We utilize sequence-level training with reinforcement learning to handle long sequence generation and alleviate the issue of exposure bias.

  • Empirical experiments show that the proposed models can effectively generate captions for figures under several metrics.

2 Related Work

Image Captioning

The existing approaches for image captioning largely fall into two categories: top-down and bottom-up. The bottom-up approaches first output key words describing different aspects of an image such as visual concepts, objects, attributes, and then combines them to sentences.  Farhadi et al. (2010); Kulkarni et al. (2011); Elliott and Keller (2013); Lebret et al. (2014); Fang et al. (2015)

lie in this category. The successful application of deep learning in natural language processing, for example, machine translation, motivates the exploration on top-down methods, such as  

Mao et al. (2014); Donahue et al. (2015); Jia et al. (2015); Vinyals et al. (2015); Xu et al. (2015)

. These approaches formulate the image captioning as a machine translation problem, directly translating an image to sentences by utilizing the encoder-decoder framework. The approaches based on deep neural networks proposed recently largely fall into this category.

Visual Question Answering

Another task related to the figure captioning problem is VQA Kafle and Kanan (2017), which is to answer queries in natural language about an image. The traditional approaches Antol et al. (2015); Gao et al. (2015); Kafle and Kanan (2016); Zhou et al. (2015); Saito et al. (2017)

train a linear classifier or neural network with the combined features from images and questions. Bilinear pooling or related techniques are further proposed to efficiently and expressively combine the image and question features

Fukui et al. (2016); Kim et al. (2016). Spatial attention was used to adaptively modify the visual features or local features in VQA Xu and Saenko (2016); Yang et al. (2016); Ilievski et al. (2016). Bayesian models were used to discover the relationships between the features of the images, questions and answers Malinowski and Fritz (2014); Kafle and Kanan (2016). Previous works Andreas et al. (2016b, a) also decompose VQA into several sub-problems and solve these sub-problems individually.

Figure VQA

VQA has been used to answer queries in natural language about figures. Kahou et al. Kahou et al. (2017) introduced FigureQA, a novel visual reasoning corpus for VQA task on figures. On this dataset, relation network Santoro et al. (2017) has strong performance among several models. Kafle et al. Kafle et al. (2018) presented DVQA, a dataset used to evaluate bar chart understanding by VQA. On this dataset, multi-output model and SAN with dynamic encoding model have been shown to achieve better performances.

Figure 1: An example in FigCAP. We generate both high-level and detailed captions for the figure.

Figure 2: Model overview: Our model takes a figure image as input, encodes it with ResNet

. Decoder is a LSTM with Attention Models

Att_F, Att_R and Att_L. Solid arrow lines show data flows, and dash arrow lines show the attentions.

3 Background

3.1 Sequence-Generation Model

A sequence-generation model generates a sequence conditioned on an object , where is a generated token at time and is the set of output tokens. The length of an output sequence is denoted as , and indicates a subsequence . The data are given with as pairs to train a sequence-generation model. We denote the output a sequence-generation model as .

Starting from the initial hidden state , a RNN produces a sequence of states , given a sequence-feature representation , where denotes a function mapping a token to its feature representation. Let . The states are generated by applying a transition function for times. The transition function

is implemented by a cell of an RNN, with popular choices being the Long Short-Term Memory (LSTM 

Hochreiter and Schmidhuber (1997)

) and Gated Recurrent Units (GRU 

Cho et al. (2014)). We use LSTM in this work. To generate a token , a stochastic output layer is applied on the current state :

where denotes one draw from a multinomial distribution, and

represents a linear transformation. Since the generated sequence

is conditioned on , one can simply start with an initial state encoded from :  Bahdanau et al. (2017); Cho et al. (2014). Finally, a conditional RNN can be trained for sequence generation with gradient ascent by maximizing the log-likelihood of a generative model.

3.2 Sequence-Level Training

Sequence-generation models are typically trained with “Teacher-Forcing”, which maximizes the likelihood estimation (MLE) of the next ground-truth word given the previous ground-truth word. This approach accelerates the convergence of training, but introduces exposure bias 

Ranzato et al. (2016), caused by the mismatch between training and testing. The error will accumulate during testing, and this problem becomes more severe when the sequence become longer.

Sequence generation with reinforcement learning (RL) can alleviate exposure bias and improve the performance by directly optimizing the evaluation metrics via sequence-level training. Instead of training in word-level as MLE, sequence-level training is guided by the reward of the sequence. Variants of this method include adding actor-critic Bahdanau et al. (2017) or self-critical baselines Rennie et al. (2016); Anderson et al. (2017) to stabilize the training. Besides, Luo et al. (2018)

used image retrieval model to discriminate the generated and reference captions combined with sequence-level training.

4 Problem Definition and Dataset

Figure Captioning

This task aims at producing descriptions with essential information for a given figure. The input is a figure and the expected output is the caption for this figure. The caption may contains high-level information only, such as figure type, number of labels, and label names. This is to give the users a rough idea of the content in the figure. Or the caption may contain more details, such as the relations among labels (e.g., A is larger than B, C has the maximum area under the curve). This is to give the users a deep understanding of the logic demonstrated in the figure. Depending on the use cases, the tasks of figure captioning can be categorized into (i) generating high-level captions for figures and (ii) generating detailed captions for figures.

FigCAP

There are some public datasets from previous work on figure understanding, such as FigureSeer Siegel et al. (2016), DVQAKafle et al. (2018) and FigureQA Kahou et al. (2017). FigureSeer contains figures from research papers, while plots in both DVQA and FigureQA are synthetic. Due to the synthetic nature, one can generate as many figures, accompanied by questions and answers as he wants. In this sense, the size of FigureSeer is relatively small compared to DVQA and FigureQA, though its figures come from real data. In terms of figure type, FigureQA contains vertical and horizontal bar charts, pie charts, line plots, and dot-line plots while DVQA has only bar charts. Also, reasoning ability is important for captioning approaches to generate good quality captions. Note that FigureQA is designed for visual reasoning task. Considering the above factors, we generate our dataset FigCAP based on FigureQA.

FigCAP consist of figure-caption pairs where figures can be generated by the method introduced in Kahou et al. (2017) and captions are based on corresponding fundamental data, i.e., they are ground truth captions (reference captions). Note that a human would obviously not describe a figure with exactly the same sentences. To increase diversity of reference captions, we design templates to paraphrase sentences. Table 1 lists selected templates we use to paraphrase sentences.

This figure includes N labels: A, B, C..; A is the maximum…
There are N labels in this TYPE; their names are A, B, C..
This is TYPE; it has N labels: A, B, C, D…; A is larger than B, B is the maximum…
This figure is TPYE; it contains N categories; their names are A, B, C, D…; A is larger than B
There are N different labels in this line plot, with labels A, B…; D has the largest area under the curve…
This figure is TYPE; there are N categories in it; their names are A, B, C…; C is the minimum…
There are N different bars in this TYPE: A, B, C, D…; C is the minimum…
It is a dot line plot, with N lines: A, B, C, D…; C is the minimum…
This figure is a dot line plot; there are N lines; their names are A, B, C…; C is the minimum…
There are N categories in this dot line plot: A, B, C…
Table 1: The selected templates for generation captions from QA dataset.

With these templates, we develop two datasets, FigCAP-H and FigCAP-D for two different use cases. FigCAP-H contains High-level descriptions for figure captions. In contrast, FigCAP-D contains Detailed descriptions for figure captions. Both FigCAP-H and FigCAP-D have five different types of figures: horizontal bar chart, vertical bar chart, pie chart, line plot and dotted line plot. The numbers of each type of figures are roughly the same for each of them. Table 2 shows the numbers of figure-caption pairs for both datasets. Their sizes are similar to the setting in (Gan et al., 2017). Note that since both figures and captions are synthetic, the figure-caption pairs can be generated as many as needed.

Datasets Training Validation Testing
FigCAP-H 99,360 5,000 5,152
FigCAP-D 99,360 5,000 5,152
Table 2: Statistics for FigCAP-H and FigCAP-D.

An example of captions for Figure 1 is the following.

This is a line plot. It contains 6 categories. Dark Magenta has the lowest value. Lawn Green has the highest value. [Sky Blue is less than Lawn Green. Yellow is greater than Violet. Sky Blue has the minimum area under the curve. Lawn Green is the smoothest. Yellow intersects Magenta.]“

The words underlined are high level captions of the figure. The words in square brackets are detailed captions of the figure, which describes the relationships among the labels of categories represented by plotted lines.

Figure captioning using the FigCAP data is more challenging than natural image captioning for two main reasons. First, the sentences in figure captioning are much longer, compared with natural image captioning. Second, the logical information is much more important and complex, yet is very difficult and challenging to extract from figures. Another important and challenging problem is how to capture the key information and insights from the figure automatically, e.g., humans can derive key insights from the figure by making inferences based on the logical and semantic information in the figure.

5 The Proposed Models

We describe the proposed model for figure captioning, as illustrated in Figure 2 . The model generally follows an encoder-decoder structure. The encoder is a Residual Network He et al. (2016) which extracts feature maps from the given figures. Reasoning network, built upon the feature maps, produces relation maps which embed logical information in the given figure. We use LSTM Hochreiter and Schmidhuber (1997) for decoding. With our proposed attention models, the decoder may optionally attend to the label maps, feature maps and/or relation maps. The objective of figure captioning is to maximize likelihood or total rewards. The details of each component will be presented in the following subsections.

5.1 Captioning Model

Similar to the approaches in  Rennie et al. (2016); Karpathy and Fei-Fei (2015), we use the following neural networks for figure captioning. The figure is used as the input of a ResNet.

The output of the ResNet (Feature Maps) is used to initialize a LSTM:

is the sigmoid function. The caption is preprocessed with a BOS token in the beginning and a EOS token in the end. We use the one-hot vector

to represent the word , and the encoding is further embedded by a linear embedding .

The word vector and context vector (See Section: Attention Models for Figure Captioning) are used as the input of the LSTM. The signals for input gate, forget gate and output gate are

respectively. is the context vector, is the sigmoid function, and is the output of hidden layer in the LSTM. With the signals for input gate, forget gate and output gate, is computed as:

where is the context vector, is the hyperbolic tangent function, and is the maxout non-linearity.

We use both the context vector and to predict the next word :

We illustrate details for computing context vector with multiple attention mechanism in next section.

5.2 Attention Models for Figure Captioning

Attention mechanism has been widely used in the encoder-decoder structure to improve the decoding performance. We propose two attention models: Relation Maps Attention (Att_R), and Label Maps Attention (Att_L). We also introduce Feature Maps Attention (Att_F). Context vector can be computed from one of them, or combination of them.

5.2.1 Feature Maps Attention Att_F

Feature Maps Attention Model takes Feature Maps ( contains feature vectors; ) and the hidden state of LSTM as input. For each feature in , it computes a score between and . With these scores as weights, it computes the context vector as the weighted sum of all features in the feature maps. Equation 1 defines Feature Maps Attention Model:

(1)

where is the -th feature in the feature maps , is the context vector and is an attention weight.

5.2.2 Relation Maps Attention Att_R

In order to generate correct captions describing relations among the labels (e.g. A is the maximum, B is greater than C, C is less than D.), it is essential to perform reasoning among labels in a given figure. Inspired by Relation Networks Santoro et al. (2017), we propose the Relation Maps Attention Model (Att_R). We consider each feature vector in the feature maps as an object. For any two “objects”, for example, and , we concatenate them and feed the vector into a MLP, resulting in a relation vector :

(2)

Therefore, the relation maps contains relation vectors ( is the number of feature vectors in feature maps ). Given the relation maps , at decoding step , Att_R computes the relation context vector as follows:

(3)

where is the -th relation vector in relation maps and is an attention weight.

Note that more complex relationships can be induced from pairwise relations, e.g. A > B and B > C lead to A > C. The relation map obtained from Reasoning Net represents abstract objects that implicitly represent object(s) in the figure, not explicitly represent one specific object like a bar or a line.

5.2.3 Label Maps Attention Att_L

We propose Label Map Attention Model (Att_L) where the LSTM attends to Label Map for decoding. Label Map is composed of embeddings of those labels appearing in the figure. If is the number of labels in the figure, then contains vectors. Let be the -th vector in the label maps , we define Att_L as follows:

(4)

where is the context vector at time step .

Note that figure labels are also used as inputs. For example, in Figure 1, is 6; Yellow, Magenta, Sky Blue, Violet, Lawn Green and Dark Magenta are extracted from it using state-of-the-art computer vision techniques such as Optical Character Recognition (OCR). Since labels appear in the caption of the input figure, instead to define a new set of vectors to represent the labels in the Label Maps , we use a subset of the word embeddings . In Figure 1, embeddings for Yellow, Magenta, Sky Blue, Violet, Lawn Green and Dark Magenta compose its Label Map .

5.2.4 Context Vector

In the captioning model, the decoder can use any combination of Att_F, Att_R and Att_L, or it can use only one of them. For example, if we incorporate all three Attention Models (Eq.1,3,4) in the caption generation model, the final context vector , used as input to the decoder, is as follows:

(5)

We explore different combinations of Attention Models for generating captions. More details are in Experimental Evaluations (Section 6).

5.3 Hybrid Training Objective

In the traditional method Williams and Zipser (1989), “Teacher forcing” is widely used for the supervised training of decoders. Given an object X, it maximizes the likelihood of the target word , given the previous target sequences :

(6)

Due to the exposure bias and indirectly optimizing the evaluation metric, supervised training usually can not provide best results. Besides, the word-level training is difficult to handle the generation with different but reasonable word-orders. As a long-text-generation task, figure captioning will accumulate more errors as more words predicted and diversity will be undermined.

Sequence-level training with RL can effectively alleviate the mentioned problems, by directly optimizing the sequence-level evaluation metric. We use the self-critical policy gradient training algorithm in our model. Specifically, a sequence is generated by greedy word search, i.e.

, selecting the word with the highest probability. Then, another sequence

is generated by sampling next word

according to the probability distribution of

. The sampled sequence is an exploration of the policy for generating the caption, and the sequence obtained from greedy search is the baseline. We use CIDEr as the sequence-level evaluation metric and compute CIDEr for and , respectively. The reward is defined as the difference of CIDEr between the sampled sequence and greedy sequence . Let be the CIDEr of sequence . We minimize the sequence-level loss (i.e. maximizing the rewards):

(7)

Our model is pretrained with MLE loss to provide more efficient policy exploration. Good explorations are encouraged while poor explorations are discouraged in future generation. However, we found that purely optimizing sequence-level evaluation metric, such as CIDEr, may lead to overfitting. To tackle this issue, we use hybrid training objective in our model, considering both word-level loss provided by MLE (Eq.6) and sequence-level loss computed by RL (Eq.7):

(8)

where is a scaling factor balancing the weights between and . In practice, starts from 1 and slowly decays to 0, then only reinforcement learning loss is used to improve our generator.

6 Experimental Evaluations

In this section, we validate our proposed models on the FigCAP-H and FigCAP-D. Specifically, we evaluate the models in two use cases: generating high-level captions and generating detailed captions for figures, respectively. We perform an ablation study on the improvements brought by each part of our proposed method.

Evaluation Metrics
Models CIDEr BLEU1 BLEU2 BLEU3 BLEU4 METEOR ROUGE
CNN-LSTM 0.232 0.332 0.255 0.201 0.157 0.188 0.270
CNN-LSTM+Att_F 0.559 0.333 0.262 0.210 0.168 0.209 0.334
CNN-LSTM+Att_F+Att_L 1.018 0.337 0.269 0.215 0.170 0.227 0.368

Table 3: Results for FigCAP-H: High-level Caption Generation.
Evaluation Metrics
Models CIDEr BLEU1 BLEU2 BLEU3 BLEU4 METEOR ROUGE
CNN-LSTM 0.158 0.055 0.050 0.044 0.038 0.115 0.244
CNN-LSTM+Att_F 0.868 0.215 0.200 0.181 0.159 0.200 0.401
CNN-LSTM+Att_F+Att_L 0.917 0.232 0.214 0.194 0.170 0.207 0.413
CNN-LSTM+Att_All 1.036 0.312 0.290 0.264 0.233 0.231 0.468
CNN-LSTM+Att_All+RL 1.179 0.404 0.367 0.324 0.270 0.263 0.489
Table 4: Results for FigCAP-D: Detailed Caption Generation. Att_All=Att_F+Att_L+Att_R.

6.1 Experimental Settings

We implement the following models with TensorFlow, and conduct experiments on a single nVidia Tesla V100 GPU. For any of them,

ResNet-50

pretrained on ImageNet 

Deng et al. (2009) is used as the encoder and a 256-unit LSTM is the decoder.

  • CNN-LSTM: This baseline model uses basic CNN-LSTM structure, without any Attention Model.

  • CNN-LSTM+Att_F: This model uses Att_F for decoding. Similar model is used in natural image captioning Xu et al. (2015).

  • CNN-LSTM+Att_F+Att_L: This model uses both Att_F and Att_L for decoding.

  • CNN-LSTM+Att_F+Att_L+Att_R: This model uses Att_F, Att_L and Att_R for decoding.

  • CNN-LSTM+Att_F+Att_L+Att_R+RL

    : The loss function of this model is described in Section 

    5.3. Training with RL can improve the model’s performance when handling long captions, which is suitable for FigCAP-D.

All of them are optimized with Adam Kingma and Ba (2014)

on the training set and evaluated on the testing set. We tune hyperparameters on the validation set. Table 

2 shows the statistics of our datasets FigCAP-H and FigCAP-D. Appendix A contains more details on experimental settings. Following Xu et al. (2015) and  Rennie et al. (2016), we use CIDEr Vedantam et al. (2015), BLEU1-4 Papineni et al. (2002), METEOR Banerjee and Lavie (2005) and ROUGEL Lin (2004) as evaluation metrics. Note that we only evaluate models containing Att_R on FigCAP-D since only long captions contain relation information.

6.2 Results of Generating High-Level Captions

We evaluate the proposed models for the task of generating high-level captions. Compared to generating the detailed captions, generating high-level descriptions is relatively easier. We do not need to model the relations between the labels in the figures. Besides, the high-level captions are usually much shorter than the detailed captions. Thus, in this task, we do not evaluate Relation Maps Attention and sequence-level training with RL.

Table 3 shows the performances of different models for generating high-level captions. It is observed that Label Maps Attention can effectively improve the model performances under different metrics. This observation indicates that, different from natural image captioning, features specific to figures, such as labels, can be utilized to boost the model’s performance.

6.3 Results of Generating Detailed Captions

We further evaluate the proposed models for the task of generating detailed captions. For generating detailed captions, it is important to discover the relations between the labels in the figures, and generate the long sequences of captions. Thus, we further validate the improvements by introducing Relation Maps Attention and sequence-level training by RL.

Table 4 shows the performances of different models in generating detailed captions. There are several observations. First, in this task we observe similar improvements using Label Maps Attention, compared to the results of high-level caption generation in Table 3. Second, in most cases the performance of CNN-LSTM+Att_F+Att_L is better in generating high-level captions, than its performance in generating detailed captions. This indicates that compared to generating high-level captions, generating detailed captions is usually more challenging: in the latter task, we need to model the relations between the labels of figures and handle the long sequence generation. Third, we achieve significant improvements when introducing Relation Maps Attention and RL. This validates that Relation Maps Attention and RL can effectively model the relations between the labels of figures and the long sequence generation, in the task of generating detailed captions.

6.4 Discussions

Experimental results show that the proposed Attention Models for figure captioning are capable of improving the quality of generated captions. Compared with the baseline model CNN-LSTM, we observe that models that use Attention Models achieve better performance on both FigCAP-H and FigCAP-D. This result indicates that attention-based models are useful for figure captioning. Second, we found that the effects of Att_F is more higher in FigCAP-D than FigCAP-H. It indicates that generating high-level descriptions does not actually need complex Attention Models since it is more likely a classification task which can be accomplished based on general information of the figure. In addition, we find that Relation Maps are useful if descriptions about relations of a figure’s labels are desired (e.g., Bar A is higher than Bar B; Bar C has the largest value). Furthermore, with RL we can alleviate the exposure bias issue and directly optimize the evaluation metric used at the inference time. This enables us to achieve better performance in the generation of long captions.

7 Conclusion

In this work, we investigated the problem of figure captioning. First, we presented a new dataset, FigCAP, for this figure captioning task, based on FigureQA. Second, we propose two novel attention mechanisms. To achieve accurate generation of labels in figures, we propose Label Maps Attention. To discover the relations between figure labels, we propose Relation Maps Attention. Third, to handle long sequence generation and alleviate the issue of exposure bias, we utilize sequence-level training with reinforcement learning. Experimental results show that the proposed models can effectively generate captions for figures under several metrics.

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