Re-evaluating Automatic Metrics for Image Captioning

Introduction

There has been a growing interest in research on integrating vision and language in natural language processing and computer vision communities. As one of the key problems in this emerging area, image captioning aims at generating natural descriptions of a given image [Bernardi et al., 2016]. This is a challenging problem since it requires the ability to not only understand the visual content, but also to generate a linguistic description of that content. In this regard, it can be framed as a machine translation task where the source language denotes the visual domain and the target language is a specific language such as English. The recently proposed deep image captioning studies follow this interpretation and model the process via an encoder-decoder architecture [Vinyals et al., 2015, Xu et al., 2015, Karpathy and Fei-Fei, 2015, Jia et al., 2015]. These approaches have attained considerable success in the recent benchmarks such as flickr8k [Hodosh et al., 2013], flickr30k [Young et al., 2014] and ms coco [Lin et al., 2014] as compared to the earlier techniques which explicitly detect objects and generate descriptions by using surface realization techniques [Kulkarni et al., 2013, Li et al., 2011, Elliott and Keller, 2013].

With the size of the benchmark datasets becoming larger and larger, evaluating image captioning models has become increasingly important. Human-based evaluations become obsolete as they are costly to acquire and, more importantly, not repeatable. Automatic evaluation metrics are employed as an alternative to human evaluation in both developing new models and comparing them against the state-of-the-art. These metrics compute a score that indicates the similarity/dissimilarity between an automatically generated caption and a number of human-written reference (gold standard) descriptions.

Some of these automatic metrics such as bleu [Papineni et al., 2002], rouge [Lin, 2004], meteor [Banerjee and Lavie, 2005], and TER [Snover et al., 2006] have originated from the readily available metrics for machine translation and/or text summarization. On the contrary, the more recent metrics such as cider [Vedantam et al., 2015] and spice [Anderson et al., 2016] are specifically developed for image caption evaluation task.

Evaluation with automatic metrics has some challenges as well. As previously analyzed in [Elliott and Keller, 2014], the existing automatic evaluation measures have proven to be inadequate in successfully mimicking the human judgements for evaluating the image descriptions. The latest evaluation results of 2015 ms coco Challenge on image captioning has also revealed some interesting findings in line with this observation [Vinyals et al., 2016]. In the challenge, the recent deep models outperform the human upper bound according to automatic measures, yet they could not beat the humans when the subjective human judgements are considered. These demonstrate that we need to better understand the drawbacks of existing automatic evaluation metrics. This motivates us to present an in-depth analysis of the current metrics employed in image description evaluation.

We first review bleu, rouge, meteor, cider and spice metrics, and discuss their main drawbacks. In this context, we additionally describe wmd metric which has been recently proposed as a distance measure between text documents in [Kusner et al., 2015]. We then investigate the performance of these automatic metrics through different experiments. We analyze how well these metrics mimic human assessments by estimating their correlations with the collected human judgements. Different from the previous related work [Elliott and Keller, 2014, Vedantam et al., 2015, Anderson et al., 2016], we perform a more accurate analysis by additionally reporting the results of Williams significance test. This further allows us to figure out the differences and/or similarities between a pair of metrics, whether any two metrics complement each other or provide similar results. We then test the ability of these metrics to distinguish certain pairs of captions from one another in reference to a ground truth caption. Next, we carry out an analysis on robustness of these metrics by analyzing how well they cope with the distractions in the descriptions [Hodosh and Hockenmaier, 2016].

Evaluation Metrics

A summary of the metrics investigated in our study is given in Table 1. All these metrics except spice and wmd define the similarity over words or $n$-grams of reference and candidate descriptions by considering different formulas. On the other hand, spice [Anderson et al., 2016] considers a scene-graph representation of an image by encoding objects, their attributes and relations between them, and wmd leverages word embeddings to match groundtruth descriptions with generated captions.

bleu [Papineni et al., 2002] is one of the first metrics that have been in use for measuring similarity between two sentences. It has been initially proposed for machine translation, and defined as the geometric mean of $n$-gram precision scores multiplied by a brevity penalty for short sentences. In our experiments, we use the smoothed version of bleu as described in [Lin and Och, 2004].

2 rouge

rouge [Lin, 2004] is initially proposed for evaluation of summarization systems, and this evaluation is done via comparing overlapping $n$-grams, word sequences and word pairs. In this study, we use rouge-l version, which basically measures the longest common subsequences between a pair of sentences. Since rouge metric relies highly on recall, it favors long sentences, as also noted by [Vedantam et al., 2015].

3 meteor

meteor [Banerjee and Lavie, 2005] is another machine translation metric. It is defined as the harmonic mean of precision and recall of uni-gram matches between sentences. Additionally, it makes use of synonyms and paraphrase matching. meteor addresses several deficiencies of bleu such as recall evaluation and the lack of explicit word matching. $n$-gram based measures work reasonably well when there is a significant overlap between reference and candidate sentences; however they fail to spot semantic similarity when the common words are scarce. meteor handles this issue to some extent using WordNet-based synonym matching, however just looking at synonyms may be too restrictive to capture overall semantic similarity.

4 cider

cider [Vedantam et al., 2015] is a recent metric proposed for evaluating the quality of image descriptions. It measures the consensus between candidate image description $c_{i}$ and the reference sentences, which is a set $S_{i}=\{s_{i1},\ldots,s_{im}\}$ provided by human annotators. For calculating this metric, an initial stemming is applied and each sentence is represented with a set of 1-4 grams. Then, the co-occurrences of $n$-grams in the reference sentences and candidate sentence are calculated. In cider, similar to tf-idf, the $n$-grams that are common in all image descriptions are down-weighted. Finally, the cosine similarity between $n$-grams (referred as cidern) of the candidate and the references is computed.

cider is designed as a specialized metric for image captioning evaluation, however, it works in a purely linguistic manner, and only extends existing metrics with tf-idf weighting over $n$-grams. This sometimes causes unimportant details of a sentence to be weighted more, resulting in a relatively ineffective caption evaluation.

5 spice

Another recently proposed metric for evaluating image caption similarity is spice [Anderson et al., 2016]. It is based on the agreement of the scene-graph tuples [Johnson et al., 2015, Schuster et al., 2015] of the candidate sentence and all reference sentences. Scene-graph is essentially a semantic representation that parses the given sentence to semantic tokens such as object classes $C$, relation types $R$ and attribute types $A$. Formally, a candidate caption $c$ is parsed into a scene-graph as

where $G(c)$ denotes the scene graph of caption $c$, $O(c)\subseteq C$ is the set of object mentions, $E(c)\subseteq{O(c)\times R\times O(c)}$ is the set of hyper-edges representing relations between objects, and $K(c)\subseteq O(c)\times A$ is the set of attributes associated with objects. Once the parsing is done, a set of tuples is formed by using the elements of $G$ and their possible combinations. spice score is then defined as the $F_{1}$-score based on the agreement between the candidate and reference caption tuples. For tuple matching, spice uses WordNet synonym matching [Pedersen et al., 2004] as in meteor [Banerjee and Lavie, 2005]. One problem is that the performance becomes quite dependent on the quality of the parsing. Figure 1 illustrates an example case of failure. Here, swimming is parsed as an object, with all its relations, and dog is parsed as an attribute.

6 wmd

Two captions may not share the same words or any synonyms; yet they can be semantically similar. On the contrary, two captions may include similar objects, attributes or relations yet they may not be semantically similar. Metrics that are currently in use fail to correctly identify and assess the quality of such cases. To address this issue, we propose to use a recently introduced document distance measure called Word Mover’s Distance (wmd) [Kusner et al., 2015] for evaluating image captioning approaches. wmd casts the distance between documents as an instance of Earth Mover’s Distance (emd) [Rubner et al., 2000], where travel costs are calculated based on word2vec [Mikolov et al., 2013] embeddings of the words.

For wmd, text documents (in our case image captions) are first represented by their normalized bag-of-words (nbow) vectors, accounting for all words except stopwords. More formally, each text document is represented as vectors $\mathbf{d}\in R^{n}$, where, $d_{i}=\frac{c_{i}}{\Sigma_{j=1}^{n}c_{j}}$ if a word $i$ appears $c_{i}$ times in the document. wmd incorporates semantic similarity between individual word pairs into the document similarity metric, by using the distances in word2vec embedding space. Specifically, the distance between word $i$ and word $j$ in two documents is set as the Euclidean distance between each of the corresponding word2vec embeddings $x_{i}$ and $x_{j}$, i.e., $c(i,j)={\|x_{i}-x_{j}\|}_{2}$.

The distances between words serve as building blocks to define distances between documents, hence captions. The flow between word vectors is defined with the sparse flow matrix $\mathbf{T}\in R^{n\times n}$, with $T_{ij}$ representing the travel amount of word $i$ to word $j$. The distance between two documents is then defined with $\Sigma_{i,j}{{T_{ij}}c(i,j)}$, i.e. the minimum cumulative cost required to move all words between documents. This minimum cumulative cost is found by solving the corresponding linear optimization problem, which is cast as a special case of emd metric [Rubner et al., 2000]. An example matching result is shown in Figure 2. By using word2vec embeddings, semantic similarities between words are more accurately identified. In our experiments, we convert the distance scores to similarities by using a negative exponential.

7 Drawbacks of the metrics

In order to illustrate the drawbacks of these automatic evaluation metrics, we provide an example case in Table 2. In this table, an original caption is given, together with the upper bound values for each metric, i.e. when this original caption is compared to itself. The second line includes a candidate caption that is semantically very similar to the original one and the corresponding similarity scores according to evaluation metrics. We then modify the candidate sentence slightly and observe how the metric scores are affected from these small modifications. First, we observe that all the scores decrease when some words are replaced with their synonyms. The change is especially significant for spice and cider. In this example, failure of spice is likely due to incorrect parsing or the failure of synonym matching. On the other hand, failure of cider is likely due to unbalanced tf-idf weighting. Second, we observe that the metrics are not affected much from the introduction of additional (redundant) words in the sentences. However, when the order of the words are changed, we see that bleu, rouge and cider scores decrease notably, due to their dependence on $n$-gram matching. Note that, wmd and spice are not influenced from the change in word order.

Evaluation and Discussion

A common way of assessing the performance of a new automatic image captioning metric is to analyze how well it correlates with human judgements of description quality. However, in the literature, there is no consensus on which correlation coefficient is best suited for measuring the soundness of a metric in this way. Elliott and Keller [Elliott and Keller, 2014] reports Spearman’s rank correlation, which measures a monotonic relation, whereas Anderson et al. [Anderson et al., 2016] suggests to use Pearson’s correlation, which assumes that the relation is linear, and Kendall’s correlation, which is another rank correlation measure.

The above correlation analysis is a well-established practice for automatic metric evaluation, but it is not complete in the sense that it is not meaningful to draw conclusions from it about the differences or similarities between a pair of metrics. That is, comparing the corresponding correlations relative to each other does not say much since they are both computed on the same dataset, and thus not independent. To address this issue, Graham and Baldwin [Graham and Baldwin, 2014] have suggested to use Williams significance test [Williams, 1959], which also takes into account the degree to which the two metrics correlate with each other, and can reveal whether one metric significantly outperforms the other. The test has shown to be valuable for evaluation of document and segment-level machine translation [Graham and Baldwin, 2014, Graham et al., 2015, Graham and Liu, 2016] and summarization metrics [Graham, 2015]. In this study, we extend the previous correlation-based evaluations of image captioning metrics by providing a more conclusive analysis based on Williams significance test.

Williams test [Williams, 1959] calculates the statistical significance of differences in dependent correlations, and formulated as testing whether the population correlation between $X_{1}$ and $X_{3}$ equals the population correlation between $X_{2}$ and $X_{3}$:

where $r_{ij}$ is the correlation between $X_{i}$ and $X_{j}$, and $n$ is the size of the population, with

To analyze statistical significance in the automatic metrics listed in Section 2, we use the publicly available flickr-8k [Elliott and Keller, 2014] and composite [Aditya et al., 2015] datasets, which we describe below. We note that in our experiments, we first lowercase and tokenize the candidate and reference captions using ptbtokenizer.py script from ms coco evaluation toolshttps://github.com/peteanderson80/coco-caption. We use the implementations of the metrics from the same evaluation kit with the exception of wmd. For the wmd metric, we employ the code provided by Kusner et al. [Kusner et al., 2015]https://github.com/mkusner/wmd.

flickr-8khttps://github.com/elliottd/compareImageDescriptionMeasures dataset contains quality judgements for 5822 candidate sentences for the images in its test set [Hodosh et al., 2013]. These judgements are collected from 3 human experts and they are on a scale of $$, with a score of 1 denoting a description totally unrelated to the image content, and 4 meaning a perfect description for the image. Candidate captions are all obtained from a retrieval based model, hence they are grammatically correct.

compositehttps://imagesdg.wordpress.com/image-to-scene-description-graph dataset contains human judgements for 11,985 candidate captions for the subsets of flickr-8k [Hodosh et al., 2013], flickr-30k [Young et al., 2014] and ms coco [Lin et al., 2014] datasets. The AMT workers were asked to judge the candidate caption for an image using two aspects: (i) correctness, and (ii) thoroughness of the candidate caption, both on a scale of $$ where 1 means not relevant/less detailed and 5 denotes the candidate caption perfectly describing the image. Candidate captions were sampled from the human reference captions and the captioning models in [Aditya et al., 2015, Karpathy and Fei-Fei, 2015].

Table 3 shows Pearson’s, Spearman’s and Kendall’s correlation of the metrics with the human judgements in flickr-8k and composite datasets. For flickr-8k, we follow the methodology in [Elliott and Keller, 2014] and compute correlations with the human expert scores. On the other hand. for composite, we report the mean of the correlations with correctness and thoroughness scores. In terms of these correlations, while spice produces the highest quality comparisons in flickr-8k, wmd and meteor give better results in composite in general. However, if one further inspects the score distributions of the metrics (on flickr-8k dataset) shown in Figure 3, while spice can identify irrelevant captions remarkably well, it can not effectively distinguish bad captions from relatively better ones.

In Figure 4(a), we show Spearman’s correlation between each pair of metrics, where the metrics are ordered from highest to lowest correlation with human judgementsHere, we only report Spearman’s correlation since, compared to Pearson’s, it provides a more consistent ranking of the metrics across the two datasets, and is similar to Kendall’s correlation.. Overall, the pairwise correlations are generally high for both datasets. We additionally observe that the metrics which depend on similar structures are grouped together using these correlations. For example, the $n$-gram based metrics bleu and rouge provide scores that are highly correlated with each other for flickr-8k. The correlations within composite dataset are even very high for all the metrics that consider $n$-grams, namely bleu, cider, meteor and rouge. On the other hand, the correlations of these metrics against spice and wmd are not that high. Moreover , the pairwise correlations between spice and wmd are relatively low as well. All these findings suggest that these three groups of metrics, the $n$-gram based metrics, the scene-graph based spice and the word embedding based wmd, can be complementary to each other.

Finally, in Figure 4(b), we provide the results of Williams significance test, which compares two different metrics with respect to their correlations against human judgements. Our results show that all the metric pairs have a significant difference in correlation with human judgement at $p<0.05$. This reveals that the pair of metrics which has close correlation scores with human judgements (e.g. spice and wmd in flickr-8k dataset) are found to be statistically different than each other. These findings collectively support our previous conclusion that all metrics considered here can complement each other in evaluating the quality of the generated captions.

2 Accuracy

In this section, following the methodology introduced in [Vedantam et al., 2015], we analyze the ability of each metric to discriminate certain pair of captions from one another in reference to a groundtruth caption. We employ the human consensus scores while evaluating the accuracies. In particular, for evaluation, a triplet of descriptions, one reference and two candidate descriptions, is shown to human subjects and they are asked to determine the candidate description that is more similar to the reference. A metric is accurate if it provides a higher score to the description chosen by the human subject as being more similar to the reference caption. For this analysis, we carry out our experiments on pascal-50s and abstract-50s datasetshttp://vrama91.github.io/cider. We consider different kinds of pairs such as (human-human correct) HC, (human-human incorrect) HI, (human-machine) HM, and (machine-machine) MM. As the candidate sentences are generated by both humans and machines, each test scenario has a different level of difficulty.

abstract-50s [Vedantam et al., 2015] dataset is a subset of the Abstract Scenes Dataset [Zitnick and Parikh, 2013], which includes 500 images containing clipart objects in everyday scenes. Each image is annotated with 50 different descriptions. For evaluation, 48 of these 50 descriptions are used as reference descriptions and the remaining 2 descriptions are employed as candidate descriptions. For 400 pairs of these descriptions, human consensus scores are available, with the first 200 are for HC and the remaining 200 are for HI.

pascal-50s [Vedantam et al., 2015] dataset is an extended version of the Pascal Sentences [Farhadi et al., 2010] dataset that contains 1000 images from PASCAL Object Detection challenge [Everingham et al., 2010] of 20 different object classes like person, car, horse, etc. This version includes 50 captions per image and human judgements for 4000 candidate pairs for the aforementioned binary-forced choice task, which are all collected through Amazon Mechanical Turk (AMT). For this dataset, all four different categories are available, having 1000 pairs for each category.

In Table 4, we present caption-level classification accuracy scores of automatic evaluation metrics at matching human consensus scores. On abstract-50s dataset, the cider metric outperforms all other metrics in both HC and HI cases. On the other hand, on pascal-50s dataset, the wmd metric gives the best scores in three out of four cases. Especially, it is the most accurate metric at matching human judgements on the challenging MM and HC cases, which require distinguishing fine-grained differences between descriptions. On average, the performances of all the other metrics are very similar to each other.

3 Robustness

In this section, we evaluate the robustness of the automatic image captioning metrics. For this purpose, we employ the binary (two-alternative) forced choice task introduced in [Hodosh and Hockenmaier, 2016] to compare the existing image captioning models. For a given image, this task involves distinguishing a correct description from its slightly distracted incorrect versions. In our case, a robust image captioning metric should always choose the correct caption over the distracted ones.

In our experiments, we use the datahttp://nlp.cs.illinois.edu/HockenmaierGroup/Papers/VL2016/HodoshHockenmaier16_BinaryTasks_Data.tar provided by the authors for a subset of flickr-30k [Hodosh et al., 2013]. Specifically, we consider four different types of distractions for the image descriptions, namely 1) Replace-Scene, 2) Replace-Person, 3) Share-Scene, and 4) Share-Person, which results 15548 correct and distracted caption pairs in total. For Replace-Scene and Replace-Person tasks, the distracted descriptions were artificially constructed by replacing the main actor (first person) and the scene in the original caption by random person and scene elements, respectively. For Share-Scene and Share-Person tasks, the distracted captions were selected from the sentences from the training part of flickr-30k [Young et al., 2014] dataset whose actor or scene chunks share the similar main actor or scene elements with the correct description. Figure 5 presents an example image together with the original description and its distracted versions.

We compare each correct caption available for an image with the remaining correct and distracted captions for that image by considering tested evaluation metrics, and then estimate an average accuracy score. In Table 5, we present the classification accuracies of the evaluation metrics for each distraction type. As can be seen, the wmd metric gives the best results for three out of four categories, and provides the second best result for the Replace-Scene case. Overall, meteor and cider metrics seem to be also robust to these distractions. The very recently proposed spice metric performs the worst for this task. This is somewhat expected as it is even affected by the use of synonyms of the words as we have previously shown in Table 2.

4 Discussion

As the experiments on quality, accuracy and robustness tests demonstrate in Sections 3.1-3.3, existing automatic image captioning metrics all have some strengths and weaknesses due to their design choices. For example, while spice, meteor and wmd give the best performances in terms of our correlation analysis against human judgements, cider and wmd provide the best classification scores for our accuracy experiments. Moreover, cider, meteor and wmd are found to be less affected by the distractors. Overall, our analysis suggests that the recently proposed wmd document metric is also quite effective for image captioning since it has high correlations with the human scores, is much less sensitive to synonym swapping and additionally performs well at the accuracy and distraction tasks.

Our analysis also shows that the existing metrics both theoretically and empirically differ from each other with significant differences. Compared to the recent results of significance testing of machine translation and summarization metrics [Graham and Baldwin, 2014, Graham et al., 2015, Graham and Liu, 2016, Graham, 2015], our results suggest that there remains much room for improvement in developing more effective image captioning evaluation metrics. We leave this for future work, but a very naive idea would be combining different metrics into a unified metric and we simply test this idea using score combination, after normalizing the score of each metric to the range $$. Among all possible combinations, we find that the combination of wmd+spice+meteor performs the best with a Spearman’s correlation of 0.66 for flickr-8k and 0.45 for composite dataset, yielding an improvement from spice (0.64 and 0.42). In addition, we should add that this unified metric significantly outperforms the individual metrics according to Williams test ($p<0.01$).

Conclusion

In this paper, we provide a careful evaluation of the automatic image captioning metrics, and propose to use wmd, which utilizes word2vec embeddings of the words to compute a semantic similarity of sentences. We highlight the drawbacks of the existing metrics, and we empirically show that they are significantly different than each other. We hope that this work motivates further research into developing better evaluation metrics, probably learning based ones, as previously studied in machine translation literature [Kotani and Yoshimi, 2010, Guzmán et al., 2015]. We also observe that incorporating visual information (via Scene-graph used by spice) and semantic information (via wmd) is useful for the caption evaluation task, which motivates the use of multimodal embeddings [Kottur et al., 2015].

Acknowledgments

We thank the anonymous reviewers for their valuable comments. This work is supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK), with award no 113E116.

References