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| \begin{document} | |
| \title{Show and Tell: A Neural Image Caption Generator} | |
| \author{Oriol Vinyals\\ | |
| Google\\ | |
| {\tt\small vinyals@google.com} | |
| \and | |
| Alexander Toshev\\ | |
| Google\\ | |
| {\tt\small toshev@google.com} | |
| \and | |
| Samy Bengio\\ | |
| Google\\ | |
| {\tt\small bengio@google.com} | |
| \and | |
| Dumitru Erhan\\ | |
| Google\\ | |
| {\tt\small dumitru@google.com} | |
| } | |
| \maketitle | |
| \begin{abstract} | |
| Automatically describing the content of an image is a fundamental | |
| problem in artificial intelligence that connects | |
| computer vision and natural language processing. | |
| In this paper, we present a generative model based on a deep recurrent | |
| architecture that combines recent advances in computer vision and | |
| machine translation and that can be used to generate natural sentences | |
| describing an image. The model is trained | |
| to maximize the likelihood of the target description | |
| sentence given the training image. Experiments on several datasets show | |
| the accuracy of the model and the fluency of the language it learns | |
| solely from image descriptions. Our model is often quite accurate, | |
| which we verify both qualitatively and quantitatively. | |
| For instance, while the current state-of-the-art BLEU-1 score (the higher the | |
| better) on the Pascal dataset is 25, our approach yields 59, to be compared to | |
| human performance around 69. We also show BLEU-1 score improvements | |
| on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. | |
| Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is | |
| the current state-of-the-art. | |
| \end{abstract} | |
| \section{Introduction} | |
| \label{sec:intro} | |
| Being able to automatically describe the content of an image using properly | |
| formed English sentences is a very challenging task, but it could have great | |
| impact, for instance by helping visually impaired people better understand the | |
| content of images on the web. This task is significantly harder, for example, than the | |
| well-studied image classification or object recognition tasks, | |
| which have been a main focus in the computer vision community~\cite{ILSVRCarxiv14}. | |
| Indeed, a description must capture not only the objects contained in an image, but | |
| it also must express how these objects relate to each other as | |
| well as their attributes and the activities they are involved in. Moreover, the above | |
| semantic knowledge has to be expressed in a natural language like English, which | |
| means that a language model is needed in addition to visual understanding. | |
| Most previous attempts have proposed | |
| to stitch together existing solutions of the above sub-problems, in order to go from | |
| an image to its description~\cite{farhadi2010every,kulkarni2011baby}. In contrast, we would like to | |
| present in this work a single joint model that | |
| takes an image $I$ as input, and is trained to maximize the likelihood | |
| $p(S|I)$ of producing a target sequence of words $S = \{S_1, S_2, \ldots\}$ | |
| where each word $S_t$ comes from a given dictionary, that describes the image | |
| adequately. | |
| \begin{figure} | |
| \begin{center} | |
| \includegraphics[width=0.5\textwidth]{overview_fig_2.pdf} | |
| \end{center} | |
| \caption{\label{fig:overview} NIC, our model, is based end-to-end on a neural network consisting of a vision CNN followed by a language generating RNN. It generates complete sentences in natural language from an input image, as shown on the example above.} | |
| \end{figure} | |
| The main inspiration of our work comes from recent advances in machine translation, where the task is to transform a sentence $S$ written | |
| in a source language, into its translation $T$ in the target language, by | |
| maximizing $p(T|S)$. For many | |
| years, machine translation was also achieved by a series of separate tasks | |
| (translating words individually, aligning words, reordering, etc), but recent | |
| work has shown that translation can be done in a much simpler way using | |
| Recurrent Neural Networks | |
| (RNNs)~\cite{cho2014learning,bahdanau2014neural,sutskever2014sequence} | |
| and still reach state-of-the-art performance. | |
| An ``encoder'' RNN {\em reads} the source sentence and | |
| transforms it into a rich fixed-length vector representation, which in turn in used as the | |
| initial hidden state of a ``decoder'' RNN that {\em generates} | |
| the target sentence. | |
| Here, we propose to follow this elegant recipe, | |
| replacing the encoder RNN by a deep convolution neural network (CNN). Over the last few years it has been convincingly | |
| shown that CNNs can produce a rich representation of the input image by embedding it | |
| to a fixed-length vector, such that this representation can be used for a variety of | |
| vision tasks~\cite{sermanet2013overfeat}. Hence, it is natural to use a CNN as an | |
| image ``encoder'', by first pre-training it for an image classification task and | |
| using the last hidden layer as an input to the RNN decoder that generates sentences (see Fig.~\ref{fig:overview}). | |
| We call this model the Neural Image Caption, or NIC. | |
| Our contributions are as follows. First, we present an end-to-end system for the | |
| problem. It is a neural net which is fully trainable using stochastic | |
| gradient descent. | |
| Second, our model combines state-of-art sub-networks for vision and language models. These | |
| can be pre-trained on larger corpora and thus can take advantage of additional data. Finally, | |
| it yields significantly better performance compared to state-of-the-art approaches; | |
| for instance, on the Pascal dataset, NIC yielded a BLEU score of 59, | |
| to be compared to the current state-of-the-art of 25, while human performance | |
| reaches 69. On Flickr30k, we improve from 56 to 66, and on SBU, | |
| from 19 to 28. | |
| \section{Related Work} | |
| \label{sec:related} | |
| The problem of generating natural language descriptions from visual | |
| data has long been studied in computer vision, but mainly for | |
| video~\cite{gerber1996knowledge,yao2010i2t}. This has led to complex | |
| systems composed of visual primitive recognizers combined with a structured | |
| formal language, e.g.~And-Or Graphs or logic systems, which are | |
| further converted to natural language via rule-based systems. Such | |
| systems are heavily hand-designed, relatively brittle and have been | |
| demonstrated only on limited domains, e.g. traffic scenes or sports. | |
| The problem of still image description with natural text has gained | |
| interest more recently. Leveraging recent advances in recognition of | |
| objects, their attributes and locations, allows us to drive natural language | |
| generation systems, though these are limited in their | |
| expressivity. Farhadi et al.~\cite{farhadi2010every} use detections to | |
| infer a triplet of scene elements which is converted to text using | |
| templates. Similarly, Li et al.~\cite{li2011composing} start off with | |
| detections and piece together a final description using phrases containing | |
| detected objects and relationships. A more complex graph of detections | |
| beyond triplets is used by Kulkani et | |
| al.~\cite{kulkarni2011baby}, but with template-based text generation. | |
| More powerful language models based on language parsing | |
| have been used as well | |
| \cite{mitchell2012midge,aker2010generating,kuznetsova2012collective,kuznetsova2014treetalk,elliott2013image}. The | |
| above approaches have been able to describe images ``in the wild", | |
| but they are heavily hand-designed and rigid when it comes to text | |
| generation. | |
| A large body of work has addressed the problem of ranking descriptions | |
| for a given image | |
| \cite{hodosh2013framing,gong2014improving,ordonez2011im2text}. Such | |
| approaches are based on the idea of co-embedding of images and text in | |
| the same vector space. For an image query, descriptions are retrieved | |
| which lie close to the image in the embedding space. Most closely, neural networks are used to co-embed | |
| images and sentences together \cite{socher2014grounded} or even image crops and subsentences \cite{karpathy2014deep} but do not attempt to generate novel | |
| descriptions. In general, the above approaches cannot describe previously unseen | |
| compositions of objects, even though the individual objects might have been | |
| observed in the training data. Moreover, they avoid addressing the | |
| problem of evaluating how good a generated description is. | |
| In this work we combine deep | |
| convolutional nets for image classification \cite{batchnorm} with | |
| recurrent networks for sequence modeling | |
| \cite{hochreiter1997long}, to create a single network | |
| that generates descriptions of images. The RNN is trained in the context of | |
| this single ``end-to-end'' network. The model is inspired | |
| by recent successes of sequence generation in machine translation | |
| \cite{cho2014learning,bahdanau2014neural,sutskever2014sequence}, with | |
| the difference that instead of starting with a sentence, we provide an image | |
| processed by a convolutional net. The closest works are by Kiros et al.~\cite{kiros2013multimodal} who | |
| use a neural net, but a feedforward one, to predict the next word given the image | |
| and previous words. A recent work by Mao et al.~\cite{baidu2014} uses a recurrent | |
| NN for the same prediction task. This is very similar to the present proposal but | |
| there are a number of important differences: we use a more powerful RNN model, | |
| and provide the visual input to the RNN model directly, which makes it possible | |
| for the RNN to keep track of the objects that have been explained by the text. As | |
| a result of these seemingly insignificant differences, our system achieves | |
| substantially better results on the established benchmarks. Lastly, Kiros et al.~\cite{kiros2014} | |
| propose to construct a joint multimodal embedding space by using a powerful | |
| computer vision model and an LSTM that encodes text. In contrast to our approach, | |
| they use two separate pathways (one for images, one for text) to define a joint embedding, | |
| and, even though they can generate text, their approach is highly tuned for ranking. | |
| \section{Model} | |
| \label{sec:model} | |
| In this paper, we propose a neural and probabilistic framework to generate | |
| descriptions from images. Recent advances in statistical machine | |
| translation have shown that, given a powerful sequence model, it is | |
| possible to achieve state-of-the-art results by directly maximizing | |
| the probability of the correct translation given an input sentence in | |
| an ``end-to-end'' fashion -- both for training and inference. These | |
| models make use of a recurrent neural network | |
| which encodes the variable length input into a fixed dimensional | |
| vector, and uses this representation to ``decode'' it to the desired | |
| output sentence. Thus, it is natural to use the same approach where, | |
| given an image (instead of an input sentence in the source language), | |
| one applies the same principle of ``translating'' it into its | |
| description. | |
| Thus, we propose to directly maximize the probability of the correct | |
| description given the image by using the following formulation: | |
| \begin{equation} | |
| \theta^\star = \arg\max_\theta \sum_{(I,S)} \log p(S | I ; \theta) | |
| \label{eqn:obj} | |
| \end{equation} | |
| where $\theta$ are the parameters of our model, $I$ is an image, and | |
| $S$ its correct transcription. Since $S$ represents any sentence, its | |
| length is unbounded. Thus, it is common to apply the chain rule to | |
| model the joint probability over $S_0,\ldots,S_N$, where $N$ is the | |
| length of this particular example as | |
| \begin{equation} | |
| \log p(S | I) = \sum_{t=0}^N \log p(S_t | I, S_0, \ldots, S_{t-1}) | |
| \label{eqn:chain} | |
| \end{equation} | |
| where we dropped the dependency on $\theta$ for convenience. | |
| At training | |
| time, $(S,I)$ is a training example pair, and we optimize the sum of | |
| the log probabilities as described in~(\ref{eqn:chain}) over the | |
| whole training set using stochastic gradient descent (further training | |
| details are given in Section \ref{sec:exps}). | |
| It is natural to model $p(S_t | I, S_0, \ldots, S_{t-1})$ with a | |
| Recurrent Neural Network (RNN), where the variable number of | |
| words we condition upon up to $t-1$ is expressed by a fixed length | |
| hidden state or memory $h_t$. This memory is updated after seeing a | |
| new input $x_t$ by using a non-linear function $f$: | |
| \begin{equation}\label{eq:rnn} | |
| h_{t+1} = f(h_{t}, x_t)\;. | |
| \end{equation} | |
| To make the above RNN more concrete two crucial design choices are to be made: what is | |
| the exact form of $f$ and how are the images and words fed as inputs $x_t$. For | |
| $f$ we use a Long-Short Term Memory (LSTM) net, which has shown state-of-the art | |
| performance on sequence tasks such as translation. This model is outlined in the | |
| next section. | |
| For the representation of images, we use a Convolutional Neural Network | |
| (CNN). They have been widely used and studied for image tasks, and are | |
| currently state-of-the art for object recognition and detection. Our particular | |
| choice of CNN uses a novel approach to batch normalization and yields the | |
| current best performance on the ILSVRC 2014 classification | |
| competition~\cite{batchnorm}. Furthermore, they have been shown to | |
| generalize to other tasks such as scene classification by means of | |
| transfer learning~\cite{decaf2014}. The words are represented with an embedding | |
| model. | |
| \subsection{LSTM-based Sentence Generator} | |
| \label{sec:lstm} | |
| The choice of $f$ in (\ref{eq:rnn}) is governed by its | |
| ability to deal with vanishing and exploding gradients~\cite{hochreiter1997long}, | |
| the most common | |
| challenge in designing and training RNNs. To address this challenge, a particular form | |
| of recurrent nets, called LSTM, was introduced \cite{hochreiter1997long} | |
| and applied with great success to translation \cite{cho2014learning,sutskever2014sequence} and sequence generation \cite{graves2013generating}. | |
| \begin{figure} | |
| \begin{center} | |
| \includegraphics[width=0.85\columnwidth]{detailed_lstm_figure.pdf} | |
| \end{center} | |
| \caption{\label{fig:lstm} LSTM: the memory block contains a cell $c$ which is controlled by three gates. In blue we show the recurrent connections -- the output $m$ at time $t-1$ is fed back to the memory at time $t$ via the three gates; the cell value is fed back via the forget gate; the predicted word at time $t-1$ is fed back in addition to the memory output $m$ at time $t$ into the Softmax for word prediction.} | |
| \end{figure} | |
| The core of the LSTM model is a memory cell $c$ encoding | |
| knowledge at every time step of what inputs have been observed up to this step (see Figure~\ref{fig:lstm}) . The behavior of the cell | |
| is controlled by ``gates" -- layers which are applied multiplicatively and thus can | |
| either keep a value from the gated layer if the gate is $1$ or zero this value if the gate is $0$. | |
| In particular, three gates are being used which control whether to forget the current cell value (forget gate $f$), | |
| if it should read its input (input gate $i$) and whether to output the new cell value (output gate $o$). | |
| The definition of the gates and cell update and output are as follows: | |
| \begin{eqnarray} | |
| i_t &= &\sigma(W_{ix} x_t+ W_{im} m_{t-1}) \\ | |
| f_t &= & \sigma(W_{fx} x_t+ W_{fm} m_{t-1}) \\ | |
| o_t &= & \sigma(W_{ox} x_t + W_{om} m_{t-1}) \\ | |
| c_t &= & f_t \odot c_{t-1} + i_t \odot h(W_{cx} x_t + W_{cm} m_{t-1}) \\ | |
| m_t &= & o_t \odot c_t \\ | |
| p_{t+1} &=& \textrm{Softmax}(m_t) | |
| \end{eqnarray} | |
| where $\odot$ represents the product with a gate value, and the various $W$ | |
| matrices are trained parameters. Such multiplicative gates make it | |
| possible to train the LSTM robustly as these gates deal well with exploding and vanishing gradients \cite{hochreiter1997long}. | |
| The nonlinearities are sigmoid $\sigma(\cdot)$ and hyperbolic tangent $h(\cdot)$. | |
| The last equation $m_t$ is what is used | |
| to feed to a Softmax, which will produce a probability distribution $p_t$ over all words. | |
| \begin{figure} | |
| \begin{center} | |
| \includegraphics[width=0.75\columnwidth]{unrolled_lstm.pdf} | |
| \end{center} | |
| \caption{\label{fig:unrolled_lstm} LSTM model combined with a CNN image embedder (as defined in \cite{batchnorm}) and word embeddings. The unrolled connections between the LSTM memories are in blue and they correspond to the recurrent connections in Figure~\ref{fig:lstm}. All LSTMs share the same parameters. } | |
| \end{figure} | |
| \paragraph{Training} The LSTM model is trained to predict each word of the | |
| sentence after it has seen the image as well as all preceding words as defined by | |
| $p(S_t | I, S_0, \ldots, S_{t-1})$. For this purpose, it is instructive to think | |
| of the LSTM in unrolled form -- a copy of the LSTM memory is created for the | |
| image and each sentence word such that all LSTMs share the same parameters and the | |
| output $m_{t-1}$ of the LSTM at time $t-1$ is fed to the LSTM at time $t$ (see | |
| Figure~\ref{fig:unrolled_lstm}). All recurrent connections are transformed to feed-forward connections in the | |
| unrolled version. In more detail, if we denote by $I$ the input | |
| image and by $S=(S_0,\ldots, S_N)$ a true sentence describing this image, the | |
| unrolling procedure reads: | |
| \begin{eqnarray} | |
| x_{-1} &=& \textrm{CNN}(I)\\ | |
| x_t &=& W_e S_t, \quad t\in\{0\ldots N-1\}\quad \label{eqn:sparse}\\ | |
| p_{t+1} &=& \textrm{LSTM}(x_t), \quad t\in\{0\ldots N-1\}\quad | |
| \end{eqnarray} | |
| where we represent each word as a one-hot vector $S_t$ of dimension equal to the | |
| size of the dictionary. Note that we denote by $S_0$ a special start word and by | |
| $S_{N}$ a special stop word which designates the start and end of the sentence. | |
| In particular by emitting the stop word the LSTM signals that a complete sentence | |
| has been generated. Both the image and the words are mapped to the same space, | |
| the image by using a vision CNN, the words by using word embedding $W_e$. The image | |
| $I$ is only input once, at $t=-1$, to inform the LSTM about the image contents. We | |
| empirically verified that feeding the image at each time step as an extra input yields | |
| inferior results, as the network can explicitly exploit noise in the image and | |
| overfits more easily. | |
| Our loss is the sum of the negative log likelihood of the correct word at each step as follows: | |
| \begin{equation} | |
| L(I, S) = - \sum_{t=1}^N \log p_t(S_t) \; . | |
| \end{equation} | |
| The above loss is minimized w.r.t. all the parameters of the LSTM, the top layer of the | |
| image embedder CNN and word embeddings $W_e$. | |
| \paragraph{Inference} | |
| There are multiple approaches that can be used to generate a sentence given | |
| an image, with NIC. The first one is {\bf Sampling} where we just | |
| sample the first word according to $p_1$, then provide the corresponding | |
| embedding as input and sample $p_2$, continuing like this until we sample the | |
| special end-of-sentence token or some maximum length. | |
| The second one is {\bf BeamSearch}: iteratively | |
| consider the set of the $k$ best sentences up to time | |
| $t$ as candidates to generate sentences of size $t+1$, and keep only the | |
| resulting best $k$ of them. This better approximates | |
| $S = \arg\max_{S'} p(S'|I)$. | |
| We used the BeamSearch approach in the following experiments, with a | |
| beam of size 20. Using a beam size of 1 (i.e., greedy search) did degrade our | |
| results by 2 BLEU points on average. | |
| \section{Experiments} | |
| \label{sec:exps} | |
| We performed an extensive set of experiments to assess the effectiveness of our | |
| model using several metrics, data sources, and model architectures, in order | |
| to compare to prior art. | |
| \subsection{Evaluation Metrics} | |
| Although it is sometimes not clear whether a description should be deemed | |
| successful or not given an image, | |
| prior art has proposed several evaluation metrics. The most | |
| reliable (but time consuming) is to ask for raters to give a subjective score | |
| on the usefulness of each description given the image. In this paper, we used | |
| this to reinforce that some of the automatic metrics indeed correlate with this | |
| subjective score, following the guidelines proposed | |
| in~\cite{hodosh2013framing}, which asks the | |
| graders to evaluate each generated sentence with a scale from 1 to 4\footnote{ | |
| The raters are asked whether the image is | |
| described without any errors, described with minor errors, with a somewhat | |
| related description, or with an unrelated description, with a score of 4 being | |
| the best and 1 being the worst.}. | |
| For this metric, we set up an Amazon Mechanical Turk experiment. Each image was | |
| rated by 2 workers. The typical level of agreement between workers | |
| is $65\%$. In case of disagreement we simply average the scores and record the | |
| average as the score. For variance analysis, we perform bootstrapping | |
| (re-sampling the results with replacement and computing means/standard | |
| deviation over the resampled results). Like~\cite{hodosh2013framing} we | |
| report the fraction | |
| of scores which are larger or equal than a set of predefined thresholds. | |
| The rest of the metrics can be computed automatically assuming one has access to | |
| groundtruth, i.e.~human generated descriptions. The most commonly used metric | |
| so far in the image description literature has been the | |
| BLEU score~\cite{papineni2002}, | |
| which is a form of precision of word n-grams between generated and reference | |
| sentences~\footnote{In this literature, most previous work report BLEU-1, i.e., they only compute precision at the unigram level, whereas BLEU-n is a geometric average of precision over 1- to n-grams.}. | |
| Even though this metric has some obvious drawbacks, it has been shown to correlate | |
| well with human evaluations. In this work, we corroborate this as well, as | |
| we show in Section~\ref{sec:results}. An extensive evaluation protocol, as well | |
| as the generated outputs of our system, can be found at \url{http://nic.droppages.com/}. | |
| Besides BLEU, one can use the perplexity of the model for a given transcription | |
| (which is closely related to our objective function in (\ref{eqn:obj})). The perplexity | |
| is the geometric mean of the inverse probability for each predicted word. We | |
| used this metric to perform choices regarding model selection and hyperparameter | |
| tuning in our held-out set, but we do not report it since BLEU is always preferred | |
| \footnote{Even though it would be more desirable, optimizing for BLEU score yields | |
| a discrete optimization problem. In general, perplexity and BLEU scores are fairly | |
| correlated.}. A much more detailed discussion regarding metrics can be found in | |
| \cite{cider}, and research groups working on this topic have been reporting | |
| other metrics which are deemed more appropriate for evaluating caption. We report | |
| two such metrics - METEOR and Cider - hoping for much more discussion and research | |
| to arise regarding the choice of metric. | |
| Lastly, the current literature on image description | |
| has also been using the proxy task of ranking a set of available | |
| descriptions with respect to a given image (see for instance~\cite{kiros2014}). | |
| Doing so has the advantage that one can use known ranking metrics like recall@k. | |
| On the other hand, transforming the description generation task into a ranking | |
| task is unsatisfactory: as the complexity of images to describe grows, together | |
| with its dictionary, the number of possible sentences grows exponentially with | |
| the size of the dictionary, and | |
| the likelihood that a predefined sentence will fit a new image will go down | |
| unless the number of such sentences also grows exponentially, which is not | |
| realistic; not to mention the underlying computational complexity of evaluating | |
| efficiently such a large corpus of stored sentences for each image. | |
| The same argument has been used in speech recognition, where one has to | |
| produce the sentence corresponding to a given acoustic sequence; while early | |
| attempts concentrated on classification of isolated phonemes or words, | |
| state-of-the-art approaches for this task are now generative and can produce | |
| sentences from a large dictionary. | |
| Now that our models can generate descriptions of reasonable quality, | |
| and despite the ambiguities of evaluating an image description (where there | |
| could be multiple valid descriptions not in the groundtruth) | |
| we believe we should concentrate on evaluation metrics for the generation task | |
| rather than for ranking. | |
| \subsection{Datasets} | |
| \label{sec:data} | |
| For evaluation we use a number of datasets which consist of images and sentences in English describing these | |
| images. The statistics of the datasets are as follows: | |
| \begin{center} | |
| \begin{tabular}{|l|c|c|c|} | |
| \hline | |
| \multirow{2}{*}{Dataset name} & \multicolumn{3}{|c|}{size} \\ | |
| \cline{2-4} | |
| & train & valid. & test \\ | |
| \hline | |
| \hline | |
| Pascal VOC 2008 \cite{farhadi2010every} & - & - & 1000 \\ | |
| \hline | |
| Flickr8k \cite{rashtchian2010collecting} & 6000 & 1000 & 1000 \\ | |
| \hline | |
| Flickr30k \cite{hodoshimage} & 28000 & 1000 & 1000 \\ | |
| \hline | |
| MSCOCO \cite{lin2014microsoft} & 82783 & 40504 & 40775 \\ | |
| \hline | |
| SBU \cite{ordonez2011im2text} & 1M & - & - \\ | |
| \hline | |
| \end{tabular} | |
| \end{center} | |
| With the exception of SBU, each image has been annotated by labelers | |
| with 5 sentences that are | |
| relatively visual and unbiased. SBU consists of | |
| descriptions given by image owners when they uploaded them to Flickr. As | |
| such they are not guaranteed to be visual or unbiased and thus this dataset has more noise. | |
| The Pascal dataset is customary used for testing only after a system has been trained on | |
| different data such as any of the other four dataset. In the case of SBU, we hold | |
| out 1000 images for testing and train on the rest as | |
| used by \cite{kuznetsova2014treetalk}. Similarly, we reserve 4K random images from the | |
| MSCOCO validation set as test, called COCO-4k, and use it to report results in the following section. | |
| \subsection{Results} | |
| \label{sec:results} | |
| Since our model is data driven and trained end-to-end, and given the abundance of | |
| datasets, we wanted to answer | |
| questions such as ``how dataset size affects generalization'', | |
| ``what kinds of transfer learning it would be able to achieve'', | |
| and ``how it would deal with weakly labeled examples''. | |
| As a result, we performed experiments on five different datasets, | |
| explained in Section~\ref{sec:data}, which enabled us to understand | |
| our model in depth. | |
| \subsubsection{Training Details} | |
| Many of the challenges that we faced when training our models had to do with overfitting. | |
| Indeed, purely supervised approaches require large amounts of data, but the datasets | |
| that are of high quality have less than 100000 images. The task | |
| of assigning a description is strictly harder than object classification and | |
| data driven approaches have only recently become dominant thanks to datasets as large as ImageNet | |
| (with ten times more data than the datasets we described in this paper, with the exception of SBU). | |
| As a result, we believe that, even with the results we obtained which are quite good, the advantage | |
| of our method versus most current human-engineered approaches will only increase in the next few years as training set sizes will grow. | |
| Nonetheless, we explored several techniques to deal with overfitting. The most obvious | |
| way to not overfit is to initialize the weights of the CNN component of our system to | |
| a pretrained model (e.g., on ImageNet). We did this in all the experiments (similar to~\cite{gong2014improving}), | |
| and it did help quite a lot in terms of generalization. Another set of weights that could | |
| be sensibly initialized are $W_e$, the word embeddings. We tried initializing them | |
| from a large news corpus~\cite{mikolov2013}, but no significant gains were observed, and we decided | |
| to just leave them uninitialized for simplicity. Lastly, we did some model level overfitting-avoiding | |
| techniques. We tried dropout~\cite{zaremba2014} and ensembling models, as well as exploring the size | |
| (i.e., capacity) of the model by trading off number of hidden units versus depth. Dropout and ensembling | |
| gave a few BLEU points improvement, and that is what we report throughout the paper. | |
| We trained all sets of weights using stochastic gradient descent | |
| with fixed learning rate and no momentum. | |
| All weights were randomly initialized except for the CNN weights, | |
| which we left unchanged because changing them had a negative impact. | |
| We used 512 dimensions for the embeddings and the size of the LSTM memory. | |
| Descriptions were preprocessed with basic tokenization, keeping all words | |
| that appeared at least 5 times in the training set. | |
| \subsubsection{Generation Results} | |
| We report our main results on all the relevant datasets in Tables~\ref{tab:coco} and \ref{tab:bleu}. | |
| Since PASCAL does not have a training set, we used the system trained using MSCOCO (arguably | |
| the largest and highest quality dataset for this task). The state-of-the-art results for PASCAL | |
| and SBU did not use image features based on deep learning, so arguably a big improvement | |
| on those scores comes from that change alone. The Flickr datasets have been used | |
| recently~\cite{hodosh2013framing,baidu2014,kiros2014}, but mostly evaluated in a retrieval framework. A | |
| notable exception is~\cite{baidu2014}, where they did both retrieval and generation, and which | |
| yields the best performance on the Flickr datasets up to now. | |
| Human scores in Table~\ref{tab:bleu} were computed by comparing one of the human captions against the other four. | |
| We do this for each of the five raters, and average their BLEU scores. Since this gives a slight | |
| advantage to our system, given the BLEU score is computed against five reference sentences | |
| and not four, we add back to the human scores the average difference of having five references instead of four. | |
| Given that the field has seen significant advances in the last years, we do think | |
| it is more meaningful to report BLEU-4, which is the standard in machine translation moving forward. Additionally, | |
| we report metrics shown to correlate better with human evaluations in Table~\ref{tab:coco}\footnote{We | |
| used the implementation of these metrics kindly provided in \url{http://www.mscoco.org}.}. | |
| Despite recent efforts on better evaluation metrics \cite{cider}, our model fares strongly versus | |
| human raters. However, when evaluating our captions using human raters (see Section~\ref{sec:human}), | |
| our model fares much more poorly, suggesting more work is needed towards better metrics. | |
| On the official test set for which labels are only available through the official website, our model had a 27.2 BLEU-4. | |
| \begin{table} | |
| \centering | |
| \begin{small} | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| Metric & BLEU-4 & METEOR & CIDER \\ | |
| \hline | |
| \hline | |
| NIC & \bf{27.7} & \bf{23.7} & \bf{85.5} \\ | |
| \hline | |
| Random & 4.6 & 9.0 & 5.1 \\ | |
| Nearest Neighbor & 9.9 & 15.7 & 36.5 \\ | |
| Human & 21.7 & 25.2 & 85.4 \\ | |
| \hline | |
| \end{tabular} | |
| \end{small} | |
| \caption{Scores on the MSCOCO development set.}\label{tab:coco} | |
| \end{table} | |
| \begin{table} | |
| \centering | |
| \begin{small} | |
| \begin{tabular}{|c|c|c|c|c|} | |
| \hline | |
| Approach & PASCAL & Flickr& Flickr& SBU \\ | |
| & (xfer) & 30k & 8k & \\ | |
| \hline | |
| \hline | |
| Im2Text~\cite{ordonez2011im2text} & & & & 11 \\ | |
| TreeTalk~\cite{kuznetsova2014treetalk} & & & & 19 \\ | |
| BabyTalk~\cite{kulkarni2011baby} & 25 & & & \\ | |
| Tri5Sem~\cite{hodosh2013framing} & & & 48 & \\ | |
| m-RNN~\cite{baidu2014} & & 55 & 58 & \\ | |
| MNLM~\cite{kiros2014}\footnotemark & & 56 & 51 & \\ | |
| \hline | |
| SOTA & 25 & 56 & 58 & 19 \\ | |
| \hline | |
| NIC & \bf{59} & \bf{66} & \bf{63} & \bf{28} \\ | |
| \hline | |
| Human & 69 & 68 & 70 & \\ | |
| \hline | |
| \end{tabular} | |
| \end{small} | |
| \caption{BLEU-1 scores. We only report previous work | |
| results when available. SOTA stands for the current | |
| state-of-the-art.}\label{tab:bleu} | |
| \end{table} | |
| \footnotetext{We computed these BLEU scores with the outputs that the authors of \cite{kiros2014} kindly provided for their OxfordNet system.} | |
| \subsubsection{Transfer Learning, Data Size and Label Quality} | |
| Since we have trained many models and we have several testing sets, we wanted to | |
| study whether we could transfer a model to a different dataset, and how much the | |
| mismatch in domain would be compensated with e.g. higher quality labels or more training | |
| data. | |
| The most obvious case for transfer learning and data size is between Flickr30k and Flickr8k. The two | |
| datasets are similarly labeled as they were created by the same group. | |
| Indeed, when training on Flickr30k (with about 4 times more training data), | |
| the results obtained are 4 BLEU points better. | |
| It is clear that in this case, we see gains by adding more training data | |
| since the whole process is data-driven and overfitting prone. | |
| MSCOCO is even bigger (5 times more | |
| training data than Flickr30k), but since the collection process was done differently, there are likely | |
| more differences in vocabulary and a larger mismatch. Indeed, all the BLEU scores degrade by 10 points. | |
| Nonetheless, the descriptions are still reasonable. | |
| Since PASCAL has no official training set and was collected independently of Flickr and MSCOCO, we | |
| report transfer learning from MSCOCO (in Table~\ref{tab:bleu}). Doing transfer learning from | |
| Flickr30k yielded worse results with BLEU-1 at 53 (cf. 59). | |
| Lastly, even though SBU has weak labeling (i.e., the labels were captions and not | |
| human generated descriptions), the task is much harder with a much larger and noisier | |
| vocabulary. However, much more data is available for training. When running the MSCOCO | |
| model on SBU, our performance degrades from 28 down to 16. | |
| \subsubsection{Generation Diversity Discussion} | |
| Having trained a generative model that gives $p(S|I)$, an obvious question is | |
| whether the model generates novel captions, and whether the generated captions | |
| are both diverse and high quality. | |
| Table~\ref{tab:diversity} shows some samples when returning the N-best list from our | |
| beam search decoder instead of the best hypothesis. Notice how the samples are | |
| diverse and may show different aspects from the same image. | |
| The agreement in BLEU score between the top 15 generated sentences is 58, which is similar to that of humans among them. This indicates the amount of diversity | |
| our model generates. | |
| In bold are the sentences that | |
| are not present in the training set. If we take the best candidate, the | |
| sentence is present in the training set 80\% of the times. | |
| This is not too surprising given that the amount | |
| of training data is quite small, so it is relatively easy for the model to pick ``exemplar'' | |
| sentences and use them to generate descriptions. | |
| If we instead analyze the top 15 generated sentences, about half of the times we | |
| see a completely novel description, but still with a similar BLEU score, | |
| indicating that they are of enough quality, yet they | |
| provide a healthy diversity. | |
| \begin{table}[htb] | |
| \begin{center} | |
| \begin{tabular}{|l|}\hline | |
| A man throwing a frisbee in a park. \\ | |
| {\bf A man holding a frisbee in his hand.} \\ | |
| {\bf A man standing in the grass with a frisbee.} \\ | |
| \hline | |
| A close up of a sandwich on a plate. \\ | |
| A close up of a plate of food with french fries. \\ | |
| A white plate topped with a cut in half sandwich. \\ | |
| \hline | |
| A display case filled with lots of donuts. \\ | |
| {\bf A display case filled with lots of cakes.} \\ | |
| {\bf A bakery display case filled with lots of donuts.} \\ | |
| \hline | |
| \end{tabular} | |
| \end{center} | |
| \caption{{N-best examples from the MSCOCO test set. Bold lines indicate a novel sentence not present in the training set.}} | |
| \label{tab:diversity} | |
| \end{table} | |
| \subsubsection{Ranking Results} | |
| While we think ranking is an unsatisfactory way to evaluate description | |
| generation from images, many papers report ranking scores, | |
| using the set of testing captions as candidates to rank given a test image. | |
| The approach that works best on these metrics (MNLM), | |
| specifically implemented a ranking-aware loss. Nevertheless, | |
| NIC is doing surprisingly well on both ranking tasks (ranking descriptions | |
| given images, and ranking images given descriptions), | |
| as can be seen in | |
| Tables~\ref{tab:recall@10} and~\ref{tab:recall@1030k}. Note that for the Image Annotation task, we normalized our scores similar to what~\cite{baidu2014} used. | |
| \begin{table} | |
| \centering | |
| \begin{small} | |
| \setlength{\tabcolsep}{3pt} | |
| \begin{tabular}{|c|ccc|ccc|} | |
| \hline | |
| \multirow{2}{*}{Approach} & \multicolumn{3}{c|}{Image Annotation} & \multicolumn{3}{c|}{Image Search} \\ | |
| & R@1 & R@10 & Med $r$ & R@1 & R@10 & Med $r$ \\ | |
| \hline | |
| \hline | |
| DeFrag~\cite{karpathy2014deep} & 13 & 44 & 14 & 10 & 43 & 15 \\ | |
| m-RNN~\cite{baidu2014} & 15 & 49 & 11 & 12 & 42 & 15\\ | |
| MNLM~\cite{kiros2014} & 18 & 55 & 8 & 13 & 52 & 10 \\ | |
| \hline | |
| NIC & \bf{20} & \bf{61} & \bf{6} & \bf{19} & \bf{64} & \bf{5} \\ | |
| \hline | |
| \end{tabular} | |
| \end{small} | |
| \caption{Recall@k and median rank on Flickr8k.\label{tab:recall@10}} | |
| \end{table} | |
| \begin{table} | |
| \centering | |
| \begin{small} | |
| \setlength{\tabcolsep}{3pt} | |
| \begin{tabular}{|c|ccc|ccc|} | |
| \hline | |
| \multirow{2}{*}{Approach} & \multicolumn{3}{c|}{Image Annotation} & \multicolumn{3}{c|}{Image Search} \\ | |
| & R@1 & R@10 & Med $r$ & R@1 & R@10 & Med $r$ \\ | |
| \hline | |
| \hline | |
| DeFrag~\cite{karpathy2014deep} & 16 & 55 & 8 & 10 & 45 & 13 \\ | |
| m-RNN~\cite{baidu2014} & 18 & 51 & 10 & 13 & 42 & 16\\ | |
| MNLM~\cite{kiros2014} & \bf{23} & \bf{63} & \bf{5} & \bf{17} & \bf{57} & \bf{8} \\ | |
| \hline | |
| NIC & 17 & 56 & 7 & \bf{17} & \bf{57} & \bf{7} \\ | |
| \hline | |
| \end{tabular} | |
| \end{small} | |
| \caption{Recall@k and median rank on Flickr30k.\label{tab:recall@1030k}} | |
| \end{table} | |
| \subsubsection{Human Evaluation} | |
| \label{sec:human} | |
| Figure~\ref{fig:turk_eval_numeric} shows the result of the human evaluations | |
| of the descriptions provided by NIC, as well as a reference system and | |
| groundtruth on various datasets. We can see that NIC is better than the reference | |
| system, but clearly worse than the groundtruth, as expected. | |
| This shows that BLEU is not a perfect metric, as it does not capture well | |
| the difference between NIC and human descriptions assessed by raters. | |
| Examples of rated images can be seen in Figure~\ref{fig:turk_eval_examples}. | |
| It is interesting to see, for instance in the second image of the first | |
| column, how the model was able to notice the frisbee given its size. | |
| \begin{figure} | |
| \begin{center} | |
| \includegraphics[width=1.0\columnwidth]{turk_eval} | |
| \end{center} | |
| \vspace{-0.5cm} | |
| \caption{\label{fig:turk_eval_numeric} {\em Flickr-8k: NIC}: predictions produced by NIC on the Flickr8k test set (average score: 2.37); {\em Pascal: NIC}: (average score: 2.45); {\em COCO-1k: NIC}: A subset of 1000 images from the MSCOCO test set with descriptions produced by NIC (average score: 2.72); {\em Flickr-8k: ref}: these are results from~\cite{hodosh2013framing} on Flickr8k rated using the same protocol, as a baseline (average score: 2.08); {\em Flickr-8k: GT}: we rated the groundtruth labels from Flickr8k using the same protocol. This provides us with a ``calibration'' of the scores (average score: 3.89)} | |
| \end{figure} | |
| \begin{figure*} | |
| \begin{center} | |
| \includegraphics[width=\textwidth]{nic_rated.jpg} | |
| \vspace{-1cm} | |
| \end{center} | |
| \caption{\label{fig:turk_eval_examples} A selection of evaluation results, grouped by human rating.} | |
| \end{figure*} | |
| \subsubsection{Analysis of Embeddings} | |
| In order to represent the previous word $S_{t-1}$ as input to the decoding LSTM | |
| producing $S_t$, we use word embedding vectors~\cite{mikolov2013}, | |
| which have the advantage of | |
| being independent of the size of the dictionary (contrary to a simpler | |
| one-hot-encoding approach). | |
| Furthermore, these word embeddings can be jointly trained with the rest of the | |
| model. It is remarkable to see how the learned representations | |
| have captured some semantic from the statistics of the language. | |
| Table~\ref{tab:embeddings} shows, for a few example words, the nearest other | |
| words found in the learned embedding space. | |
| Note how some of the relationships | |
| learned by the model will help the vision component. Indeed, having ``horse'', ``pony'', | |
| and ``donkey'' close to each other will encourage the CNN to extract features that | |
| are relevant to horse-looking animals. | |
| We hypothesize that, in the extreme case where we see very few examples of a class (e.g., ``unicorn''), | |
| its proximity to other word embeddings (e.g., ``horse'') should | |
| provide a lot more information that would be completely lost with more | |
| traditional bag-of-words based approaches. | |
| \begin{table}[htb] | |
| \label{tab:embeddings} | |
| \begin{center} | |
| \begin{tabular}{|l|l|}\hline | |
| Word & Neighbors \\ \hline | |
| car & van, cab, suv, vehicule, jeep \\ | |
| boy & toddler, gentleman, daughter, son \\ | |
| street & road, streets, highway, freeway \\ | |
| horse & pony, donkey, pig, goat, mule \\ | |
| computer & computers, pc, crt, chip, compute \\ \hline | |
| \end{tabular} | |
| \end{center} | |
| \caption{{Nearest neighbors of a few example words}} | |
| \end{table} | |
| \section{Conclusion} | |
| \label{sec:conclusion} | |
| We have presented NIC, an | |
| end-to-end neural network system that can automatically view an image | |
| and generate a reasonable description in plain English. | |
| NIC is based on a convolution neural network that encodes an image into | |
| a compact representation, followed by a recurrent neural network that | |
| generates a corresponding sentence. The model is trained to maximize | |
| the likelihood of the sentence given the image. | |
| Experiments on several datasets | |
| show the robustness of NIC in terms of qualitative results (the | |
| generated sentences are very reasonable) and quantitative evaluations, | |
| using either ranking metrics or BLEU, a metric used in machine translation | |
| to evaluate the quality of generated sentences. | |
| It is clear from these experiments that, as the size of the available | |
| datasets for image description increases, so will the performance of | |
| approaches like NIC. | |
| Furthermore, it will be interesting to see how one can use unsupervised | |
| data, both from images alone and text alone, to improve image description | |
| approaches. | |
| \section*{Acknowledgement} | |
| We would like to thank Geoffrey Hinton, Ilya Sutskever, Quoc Le, Vincent Vanhoucke, and Jeff Dean for useful discussions on the ideas behind the paper, and the write up. | |
| {\small | |
| \bibliographystyle{ieee} | |
| \bibliography{egbib} | |
| } | |
| \end{document} | |