. Generating descriptions of images automatically is very useful in practice, for example, it can help visually impaired people understand image contents and improve image retrieval quality by discovering salient contents.
Even though a great success has been achieved in object recognition, describing the contents of images automatically is still a very challenging task and more difficult than visual classification. Image captioning models need to have a thorough understanding of a certain image, and capture the complicated relationships among objects and find salient ones. Moreover, they have to understand the interactions between images and languages and translate the image representations into language representations. Finally, the sentences generated to express the captured information need to be natural.
Inspired by the advance in machine translation, the encoder-decoder framework was proposed to describe image contents automatically. The encoder is usually a convolutional neural network (CNN), while decoder is a recurrent neural network (RNN). The encoder represents the image with a vector, which captures objects and semantic information, while decoder generates natural sentences by consuming the image representation. However, this vector is unlikely to capture all necessary structural information needed for generating the description in the subsequent decoding phase. Moreover, decoder steps need to generate sentences that both describe the image contents and fit the trained language model. The decoder needs to balance the two factors to avoid that either one dominates the generating process. We noticed that sometimes the generated sentence seems to be too common and only weakly related to the input image.
One direction to overcome the above limitations of the encoder-decoder framework is to introduce attention mechanisms, which steer the captioning model to focus on the important information for generating image descriptions [Xu et al.2015]. Models with attention have achieved promising performance on the image captioning tasks and some variants [Chen et al.2017, Yang et al.2016]
have also been proposed by providing better annotation vectors. Useful external information related to input images has also been introduced into the attention model to improve the performance, including[You et al.2016, Mun, Cho, and Han2017].
Another direction to address the limitations of the encoder-decoder framework is to inject useful information into the decoder at each time step. Because the decoder can access the information related images at each time step, balancing between describing the input image and fitting the language model becomes easier. Some works have followed this direction and introduced predictions of word occurrences in captions [Yao et al.2016], semantic vector [Jia et al.2015, Zhou et al.2016] into the decoder step, and promising performance have been presented. However, none of these models could learn the injected information for the decoder automatically. Hence, it could not be adaptively adjusted according to the decoder.
In this paper, we propose a novel model that can learn an extra guiding vector for the sentence generation progress automatically. The guiding vector is modeled with a guiding neural network, which is plugged into the decoder and accepts error signals from the decoder at each time step. Hence, it can be trained in a complete end-to-end way. Moreover, supervision from captions can be employed to perform discriminative training to learn a better guidance.
The related works about image captioning can be divided into three categories. We give a brief review in this section.
In the early stage of the research area of visual to language, template based methods were proposed to describe image contents automatically [Farhadi et al.2010, Kulkarni et al.2013]. This kind of methods detect attributes, scenes and objects first and fill in a sentence template, usually a triplet. Then sentences are generated with these templates. Farhadi [Farhadi et al.2010] inferred a triplet of scene elements which is converted to text using templates. Kulkarni [Kulkarni et al.2013] adopted a conditional random field (CRF) to jointly reason across objects, attributes, and prepositions before filling the templates. [Kuznetsova et al.2012, Mitchell et al.2012] use more powerful language templates, such as a syntactically well-formed tree, and add descriptive information from the output of attribute detection.
Retrieval-based methods [Kuznetsova et al.2012, Mason and Charniak2014] first retrieve visually similar images, and then transfer captions of those images to the target image. The advantage of these methods is that the generated captions are more natural than the generations by template-based methods. However, because they directly rely on retrieval results among training data, there is little flexibility for them to add or remove words based on the content of an image.
Neural network-based methods.
Recently, inspired by advance in machine translation, the encoder-decoder framework has also been applied to image caption generations [Vinyals et al.2015, Xu et al.2015, Yang et al.2016]. In this framework, a convolutional neural network (CNN) trained for an image classification task is used as the image encoder, with the last fully-connected layer as the input to a recurrent neural network (RNN) decoder, which generates each word step by step until the end of the sentence [Vinyals et al.2015]. In [Xu et al.2015], an attention mechanism was introduced into this framework. The attention mechanism could help the model focus on the subregions that are important for the current time step. In [Yang et al.2016], review steps were proposed to be inserted in the middle of the encoder and decoder, as an extension of [Xu et al.2015]. The review steps can learn the annotation vectors and initial states for the decoder steps, and the learned annotations and initial states are better than those generated by the encoder.
Except those extensions to attention mechanisms, several models have been proposed to improve the image captioning performance by introducing more useful information into the encoder-decoder framework. In [Wu et al.2016]
, the word occurrence prediction was treated as a multi-label classification problem and a region-based multi-label classification framework was proposed to extract visually semantic information. This prediction is then used to initialize a memory cell of a long short-term memory (LSTM) model. Yaodiscussed different approaches to incorporate word occurrence predictions into the decoder step [Yao et al.2016].
Recently, policy-gradient methods for reinforcement learning were combined with the encoder-decoder framework. In[Rennie et al.2017], the cross entropy loss was replaced with non-differentiable test metrics for image captioning tasks, e.g., CIDEr, and the system was trained with the REINFORCE algorithm [Williams1992]. Since it optimized the testing metrics directly, significant improvements were achieved. This method can be used to improve the performance of all encoder-decoder models.
Our method is an extension to the encoder-decoder framework. We provide a short review of encoder-decoder framework in this section.
Under the encoder-decoder framework for image captioning, a CNN is usually employed as encoder to extract global representations and subregion representations. Global representations are usually the outputs of full connecting layers and subregion representations are usually the outputs of convolutional layers. Many different CNN can be used, e.g., VGG [Simonyan and Zisserman2015], Inception V3 [Szegedy et al.2016], ResNet [He et al.2016]. In this paper, we use Inception V3 as encoder. And the extracted global representation and subregion representations are denoted as and respectively, where is the number of kernels. In this paper, is the output of last full-connected layer and are the outputs of the last convolutional layer (before pooling).
Given the image representations, a decoder is employed to translate the image into natural sentences. A decoder is a recurrent neural network (RNN), and gated recurrent unit (GRU)[Chung et al.2014] or long short-term memory [Hochreiter and Schmidhuber1997] (LSTM) can be utilized. In this paper, we use LSTM as the basic unit of the decoder. To overcome the limitations of LSTM, attention mechanisms were introduced to help the decoders focus on important parts at each time step. Attentive models can easily outperform non-attentive models. Hence, we only consider the decoders with attention mechanisms in this paper.
Recall that a LSTM with attention mechanism is a function that outputs the results based on the current hidden state, current input, and context vector. Context vector is the weighted sum of annotation vectors, and the weights are determined by a attention model. We adopt the same LSTM used in [Xu et al.2015] and express the LSTM unit with attention as follows:
where , , , and are input gate, forget gate, memory cell, output gate and hidden state of the LSTM, respectively. Here,
is a linear transformation operator,is the input signal at the -th time step and is the context vector which is the output of attention model .
The aim of image captioning is to generate a caption consisting of words given a certain image . The objective adopted is usually to minimize a negative log-likelihood:
where and . Here, is a matrix for linear transformation, is the sign for the start of sentences, and
denotes the distributed representation of the word, in which is the one-hot representation for the word and is the word embedding matrix. Let denote the attention model which usually attends to different regions of image [Xu et al.2015] during the step-by-step word generation process. The objective of such an attention model is to guide the model to focus on the important subregions at the current time step.
The Proposed Methods
In this section, we will present our model. For the existing encoder-decoder models, the input of decoder at each time step is usually the word embedding of previous word. Here, we consider to learn an informative guiding vector which can be used to compose the input for the decoder at each time step. The framework is shown in Fig. 1. Here, we use a guiding network to model the guiding vector
. Its inputs are annotations vectors from encoder and attribute features extracted from input image. The guiding network is plugged as a component into the current encoder-decoder framework and can be trained in an end-to-end way. Details are described in the following subsections.
Leanring To Guide Decoding
In the decoder step, image representations are translated into sentences. The generated sentences need to not only describe the image contents, but also fit the trained language model. Auxiliary guiding information about images or captions injected in the decoder step could help to generate better sentences [Jia et al.2015]. Here, we propose to learn guiding information from image representations or other extra inputs automatically.
In the decoder step of the conventional encoder-decoder framework, the input signal is usually just , however if we can inject guiding information about the input image, we can get better performance. For example, we can simply use
where guiding vector v is a vector that contains information of the whole image and does not change in the process of generation, which can be seen a kind of hint of captions. For example, v can be [Jia et al.2015] or predictions of word occurrences in captions [Yao et al.2016]. Here, we propose to learn the vector by a neural network , where
is the set of annotation vectors. For simplicity, we just apply a linear layer, followed by a max-pooling layer to realize the guiding network. Specifically, max-pooling is operated on the elements at the same position of outputs from the linear layer. To introduce more useful external information, we can use the concatenation to replace and form a new set of annotation vectors, where is a vector generated by some other process describing the attributes of input image. The output of guiding network is then used to compose the input for decoder. Hence error signal at each time step could be propagated back to the guiding network. Therefore, the guiding network is completed plugged into the current encoder-coder framework and the guiding vector could be learned adaptively.
Generally, the vector which introduces external information into the guiding network could be any attribute features extracted from the input image. We can just simply set it as . However, the more informative is, the better performance will be achieved. To further improve the quality of generated captions, we adopt the predictions of frequent word occurrences in captions, which has been proved to be powerful [Jia et al.2015, Fang et al.2015]. The word occurrence prediction could be obtained by the weakly-supervised approach of Multiple Instance Learning (MIL) [Viola et al.2005]. In this paper, we adopt the same model as in [Fang et al.2015]. We denote the word occurrence predictions as MIL in this paper.
Discriminative supervision has been proved to be useful in [Fang et al.2015]
. Our guiding network provides a natural way to incorporate discriminative supervision. We can just treat the elements of guiding vector as scores for words and force the words occurred in the final caption to have higher scores. We adopt the margin-based multiple label criterion and the loss function of discriminative supervision for oneimage,caption pair could be expressed as
where is the set of all frequent words in the current caption, is the dimensionality of guiding vector and is the -th element of guiding vector. Combining the negative log-likelihood in Eq. (4), we can obtain the objective function for one image,caption pair:
where is a trade-off parameter between the language model and the discriminative supervision. To reduce the non-expected effects from less common words, we use top 1,000 frequent words in the captions of training set in the discriminative training process.
Extension to Review Net
Review Net [Yang et al.2016] is an enhanced extension of the encoder-decoder framework. It performs a number of review steps with attention mechanism on the annotation vectors from encoder, and outputs a thought vector at each review step; the thought vectors are then used as the input of the attention model in the decoder step. The review step can be seen as a process of learning the initial states and annotation vectors for decoder. But the Review Net does not learn the guiding information. It is the current state-of-the-art model for image captioning.
The review step in Review Net is a special kind of decoder. The input vector at each review step is just zero vector and the parameters of LSTM unit are not shared111Generally, for the review step, the parameters of LSTM can either be shared among all the units or not. Usually, Review Net with shared parameters at review steps performs better.. We can extend our idea to the Review Net easily, and the main framework is shown in Figure 2. In this extension, we employ two guiding networks. One is for the review step and its inputs are MIL and the annotation vectors from the encoder steps. Its output, the guiding vector , is used as input for each review step. Another guiding network is for the decoder step. The newly generated annotation vectors and MIL are used as inputs, and its output is used to guide the decoder. We also use the discriminative supervision for both guiding networks in Review Net. The complete objective function for one image,caption pair could be written as:
where and are discriminative loss functions for the guiding networks in review step and decoder step respectively, and and are trade-off parameters. In practical applications, we just set them with the same value.
|Image Captioning Model||BLEU-1||BLEU-2||BLEU-3||BLEU-4||METEOR||ROUGE-L||CIDEr|
|Soft Attention [Xu et al.2015]||0.714||0.544||0.409||0.308||0.255||0.530||0.978|
|Review Net [Yang et al.2016]||0.729||0.563||0.427||0.324||0.257||0.538||1.009|
|LSTM-A5 [Yao et al.2016]||0.722||0.550||0.414||0.312||0.255||0.531||0.985|
|Text Attention [Mun, Cho, and Han2017]||0.749||0.581||0.437||0.326||0.257||-||1.024|
|Sentence Attention [Zhou et al.2016]||0.716||0.545||0.405||0.301||0.247||-||0.970|
|Attribute LSTM [Wu et al.2016]||0.740||0.560||0.420||0.310||0.260||-||0.940|
|RIC [Liu et al.2016]||0.734||0.535||0.385||0.299||0.254||-||-|
|RHN [Gu, Wang, and Chen2016]||0.723||0.553||0.413||0.306||0.252||-||0.989|
|Image Captioning Model||BLEU-1||BLEU-2||BLEU-3||BLEU-4||METEOR||ROUGE-L||CIDEr|
|Soft Attention [Xu et al.2015]||0.731||0.564||0.430||0.328||0.257||0.540||1.015|
|Review Net [Yang et al.2016]||0.738||0.575||0.440||0.336||0.262||0.547||1.046|
|LSTM-A5 [Yao et al.2016]||0.730||0.565||0.429||0.325||0.251||0.538||0.986|
|Attribute Attention [You et al.2016]||0.709||0.537||0.402||0.304||0.243||-||-|
|NIC [Vinyals et al.2015]||-||-||-||0.321||0.257||-||0.998|
In this section, we first introduce the dataset and experiment settings. Afterwards, we demonstrate the effectiveness of our proposed model, and present comparisons with the state-of-the-art image captioning models.
The MS COCO dataset222http://mscoco.org/ [Lin et al.2014] is the most popular benchmark dataset for image captioning, which contains about 123,000 images, with each image associated with five captions. Following the conventional evaluation procedure [Mun, Cho, and Han2017, Yao et al.2016, Yang et al.2016], we employ the same data split as in [Karpathy and Fei-Fei2015] for the performance comparisons, where 5,000 images as well as the associated captions are reserved as validation and test set, respectively, with the rest employed for training. MIL model are trained on the original training split which contains about 82,000 images.
For the captions, we discard all the non-alphabetic characters, transform all letters into lowercase, and tokenize the captions using white space. Moreover, all the words with the occurrences less than 5 times are replaced by the unknown token <UNK>. Thus a vocabulary consisting of 9,520 words is finally constructed. Furthermore, we truncate all the captions longer than 30 tokens.
Configurations and Settings
Inception-V3 [Szegedy et al.2016] is used as encoder to extract image representation with dimension 2048 and annotation vectors with dimension
. The LSTM size is set as 2048. The parameters of LSTM are initialized with uniform distribution in. The AdaGrad [Duchi, Hazan, and Singer2011] is applied to optimize the network, and learning rate is set as 0.01 and weight decay is set as . Early stopping strategy is used to prevent overfitting. If the evaluation measurement on validation set, specifically the CIDEr, reaches the maximum value, we terminate the training procedure and use the corresponding model for further testing.
For sentence generation in testing stage, there are two common strategies. The first one is greedy search, which choose the word with maximum probability at each time step and set it as LSTM input for next time step until the end-of-sentence sign is emitted or the maximum length of sentence is reached. The second one is the beam search strategy which selects the top-best sentences at each time step and considers them as the candidates to generate new top- best sentences at the next time step. Usually beam search strategy provides better performance [Vinyals et al.2017], hence we adopt this strategy in our experiments. And the for all experiments are set as 3.
Experimental Results and Analysis
We compare our proposed model with state-of-the-art approaches on image captioning, including Neural Image Caption (NIC) [Vinyals et al.2015], Attribute LSTM [Wu et al.2016], LSTM-A5 [Yao et al.2016], Recurrent Image Captioner (RIC) [Liu et al.2016], Recurrent Highway Network (RHN) [Gu, Wang, and Chen2016], Soft Attention model [Xu et al.2015], Attribute Attention model [You et al.2016], Sentence Attention model [Zhou et al.2016], Review Net [Yang et al.2016], and Text Attention model [Mun, Cho, and Han2017].
Following the standard evaluation process, four types of metrics are used for performance comparisons, specifically the BLEU [Papineni et al.2002], METEOR [Banerjee and Lavie2005], ROUGE-L [Lin2004], and CIDEr [Vedantam, Lawrence Zitnick, and Parikh2015]
. These metrics measure the n-gram occurrences in the generated sentence from each image captioning model and the ground truth sentence, with the consideration of the n-gram saliency and rarity. We use the official MS COCO caption evaluation scripts333https://github.com/tylin/coco-caption for the performance comparisons.
We report the performance of our method combined with Soft Attention [Xu et al.2015] and Review Net [Yang et al.2016], which are denoted as LTG-Soft-Attention and LTG-Review-Net, respectively. To prove that the learned guiding vector is better than MIL, we combine MIL with those two methods which are denoted as MIL-Soft-Attention and MIL-Review-Net, respectively. For Soft Attention, the guiding vector is replaced with MIL in Eq. (5). For Review Net, besides the above operation for the decoder steps, we also replace guiding vector with MIL in the input for the LSTM in the review steps. The settings, e.g. data split, type of encoder or metrics used, of Soft Attention [Xu et al.2015], Review Net [Yang et al.2016] and LSTM-A5 [Yao et al.2016] are different from ours. For fair comparison, we reimplemented these methods and the performance under the same settings are provided. For other methods, we report the published results. Note that Attribute LSTM [Wu et al.2016], RCI [Liu et al.2016] and RHN [Gu, Wang, and Chen2016] used VGG net, while Text Attention [Mun, Cho, and Han2017] used GoogLeNet. The performance of different models are provided in Table 1. We can see that methods with guiding information (MIL of LTG) provide better performance than their counterpart without such information, which proves that injecting useful information into the decoder helps improve the quality of captions. Moreover, our LTG-Soft-Attention and LTG-Review-Net perform better than MIL-Soft-Attention and MIL-Review-Net respectively, which shows that the learned guiding vector is more effective. LTG-Review-Net outperforms the other models on all metrics except BLEU-1 and BLEU-2 of Text Attention. Text Attention uses much more powerful ResNet [He et al.2016] as encoder, which helps to boost captioning performance. Moreover, sentences retrieved based on image similarity are used in the attention model. It seems that such attention model favors BLEU-1 and BLEU-2.
Following the common strategy [Vinyals et al.2015], ensembles of five models trained with different initializations are further employed to examine the sentence generation ability. The performance comparisons are illustrated in Table 2. For our reimplementations of Soft Attention and Review Net, we employ Inception-V3 as the image encoder, while NIC, the Attribute Attention model, and LSMT-A5 use GoogleNet, and the Sentence Attention model uses VGG. Note that the results of NIC are obtained on a different testing set. It can be observed that the results of LTG-Review-Net outperforms all the other competitor approaches on all measurements. This superiority mainly attributes to the adaptively learned guiding vector.
The qualitative results generated by the single model of LTG-Review-Net, MIL-Review-Net, MIL-Soft-Attention, Review Net, Soft Attention and LSTM-A5 are illustrated Table 3. We select some typical images on which these methods behave differently and present the captions generated and corresponding ground truth. We can see that Review Net and Soft Attention do not generate the correct captions for the first three images. The salient objects recognized by them do not even exist in the images. There is no “people” in the first image, no “hydrant” in the second image and no “man” in the third image. Besides, with the help of MIL, the ability of recognizing objects is improved. For example, Review Net and Soft Attention generate “people”, while their counterparts with MIL do not. Even with the help of MIL, Review Net failed to recognize “plane” in the first image and “car” in the third image. For the fourth image, Review Net and Soft Attention fail to recognize “statue”. For the fifth image, Review Net, MIL-Soft-Attention and Soft Attention also fail to provide satisfactory results. Moreover, LSTM-A5 also performs badly. They recognize the wrong objects for the fifth image, like Review Net and Soft Attention do. MIL-Review-Net recognizes the “luggage” in the fifth image, which should be attributed to the help from MIL. From the above analysis, we can see that the learned guiding vector could help the model to recognize the correct salient objects. We believe that that is one of the reasons why guiding network could help to improve the quality of descriptions.
|Image||Captions generated||Ground truth|
|LTG-Review-Net: a plane flying over a city with tall buildings. MIL-Review-Net: a bunch of kites flying over a city. MIL-Soft-Attention: a group of kites flying in the sky. Review Net: a group of people flying kites in the air. Soft attention: a group of people flying kites in the air. LSTM-A5: a plane flying over a city with tall buildings.||1. a distant airplane flying between two large buildings. 2. a couple of tall buildings with a large jetliner between them. 3. commercial airliner flying between two tall structures on clear day. 4. two tall buildings and a plane in between them. 5. an airplane is seen between two identical skyscrapers.|
|LTG-Review-Net: a bunch of signs that are sitting in the grass. MIL-Review-Net: a sign that is on the side of a building. MIL-Soft-Attention: a fire hydrant on the side of the road. Review Net: a group of people standing next to a red fire hydrant. Soft Attention: a street scene with a fire truck and a fire hydrant. LSTM-A5: a sign that is on the side of a road.||1. a collection of artwork leaning against a wooden fence. 2. a collection of poster arts lined up on the fence. 3. a collection of paintings against a fence outside. 4. several paintings leaning against a fence in the grass. 5. large set of personal paintings sitting on the side of a fence.|
|LTG-Review-Net: a parking lot filled with cars and motorcycles. MIL-Review-Net: a group of motorcycles parked next to each other. MIL-Soft-Attention: a group of motorcycles parked in a parking lot. Review Net: a man riding a motorcycle down a street. Soft Attention: a man riding a motorcycle down a street. LSTM-A5: a motorcycle is parked on the side of the street.||1. a couple of motor bikes and cars on a street. 2. a pair of motorcycles parked in a bike slot. 3. a busy day on the street including cars and motorcycles. 4. motorcycles parked in designated spaces in a parking lot. 5. a parking lot scene with cars and motorcycles.|
|LTG-Review-Net: a statue of a clock on top of a tower. MIL-Review-Net: a clock tower with a clock on it. MIL-Soft-Attention: a clock tower with a statue on top of it. Review Net: a black and white clock on a tower. Soft Attention: a black and white photo of a clock on top of a building. LSTM-A5: a clock tower with a statue of a man on top of it.||1. a clock tower with a statue on top of it. 2. a statue holding a glass light stands atop an old clock. 3. a clock with a statue of a woman on top. 4. a statue is standing on top of a clock on a pole. 5. a statue holding a lamp on top of a clock.|
|LTG-Review-Net: a couple of bags that are on the ground. MIL-Review-Net: a person standing next to a pile of luggage. MIL-Soft-Attention: a trash can a trash can and a trash can. Review Net: a fire hydrant sitting on the side of a road. Soft Attention: a fire hydrant sitting on the side of a street. LSTM-A5: a street scene with a trash can and a garbage can.||1. a couple of bags of someone’s belongings that were left unattended. 2. luggage hidden behind a fence while a man makes a cell phone call 3. a piece of luggage sitting up against a gray wall. 4. a person with a backpack is standing next to a doorway with luggage on the other side. 5. a man on the phone near luggage by a gate.|
The effect of discriminative supervision.
We study the effect of the discriminative supervision in this subsection. We set the same value for and in Eq. (8) and train the LTG-Review-Net with different trade-off parameters. The performance444The greedy search was used and the average performance of 8 models with different initialization was reported. on validation set are presented in Table 3. We can see that the discriminative training with an appropriate weight do help improve performance. Our method is not very sensitive to the weight of discriminative loss and was used in all our experiments.
|Model||Number of Words|
Contributions of MIL and Annotation Vectors.
We set either MIL or annotation vectors to be zero vector to study their contributions for the performance. For LTG-Review-Net, we only consider the annotation vectors generated by encoder and the learned annotation vectors by review steps are not considered in this experiment. The performance of different models under different settings is listed in Table 4. We can see that it is difficult to tell which one is more important since the performance of models with either MIL or annotation vectors is close. Moreover, the guiding network without MIL and annotations performed the worst and guiding network with both performed the best. It is quite natural since more input information usually leads to better performance. If more informative features are provided, we can just concat them with the current inputs of the guiding network to form new inputs.
Richness of Words.
We computed the number of distinct words in the captions generated by different models on the testing set and the results are presented in Table 5. We can see that the combinations of guiding network with Review Net and Soft Attention generate much more words than the other models. This phenomenon shows that guiding network might help decoder to select more words with higher accuracies. This should be attributed to that the learned guiding vector contains language information.
In this work, we proposed to learn a guiding vector with a guiding neural network for the decoder step. The learned guiding vector is used to compose the input of the decoder. This guiding network can be plugged into the current encode-decoder framework and the guiding vector can be learned adaptively, making itself to embed information of both image and language. Thus, more information is injected into the decoder so that balancing the factors of describing the input image and fitting the language model during the caption generation process becomes easier, which help improve the quality of captions generated. The experiments performed on the MS COCO dataset verify the advantages of our approach.
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