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| \icmltitlerunning{Neural Image Caption Generation with Visual Attention} | |
| \begin{document} | |
| \twocolumn[ | |
| \icmltitle{Show, Attend and Tell: Neural Image Caption\\Generation with Visual Attention} | |
| \icmlauthor{Kelvin Xu}{kelvin.xu@umontreal.ca} | |
| \icmlauthor{Jimmy Lei Ba}{jimmy@psi.utoronto.ca} | |
| \icmlauthor{Ryan Kiros}{rkiros@cs.toronto.edu} | |
| \icmlauthor{Kyunghyun Cho}{kyunghyun.cho@umontreal.ca} | |
| \icmlauthor{Aaron Courville}{aaron.courville@umontreal.ca} | |
| \icmlauthor{Ruslan Salakhutdinov}{rsalakhu@cs.toronto.edu} | |
| \icmlauthor{Richard S. Zemel}{zemel@cs.toronto.edu} | |
| \icmlauthor{Yoshua Bengio}{find-me@the.web} | |
| \vskip 0.3in | |
| ] | |
| \begin{abstract} | |
| Inspired by recent work in machine translation and object detection, we | |
| introduce an attention based model that automatically learns to describe the | |
| content of images. We describe how we can train this model in a deterministic | |
| manner using standard backpropagation techniques and stochastically by | |
| maximizing a variational lower bound. We also show through visualization how | |
| the model is able to automatically learn to fix its gaze on salient objects | |
| while generating the corresponding words in the output sequence. | |
| We validate the use of attention with state-of-the-art performance on three | |
| benchmark datasets: Flickr8k, Flickr30k and MS COCO. | |
| \end{abstract} | |
| \section{Introduction} | |
| Automatically generating captions of an image is a task very close to the heart | |
| of scene understanding --- one of the primary goals of computer vision. Not only | |
| must caption generation models be powerful enough to solve the computer vision | |
| challenges of determining which objects are in an image, but they must also be | |
| capable of capturing and expressing their relationships in a natural language. | |
| For this reason, caption generation has long been viewed as a difficult | |
| problem. It is a very important challenge for machine learning | |
| algorithms, as it amounts to mimicking the remarkable human ability to compress | |
| huge amounts of salient visual infomation into descriptive language. | |
| Despite the challenging nature of this task, there has been a | |
| recent surge of research interest in attacking the image caption generation | |
| problem. Aided by advances in training neural networks \citep{Krizhevsky2012} | |
| and large classification datasets \citep{Imagenet14}, recent work has | |
| significantly improved the quality of caption generation using a combination of | |
| convolutional neural networks (convnets) to obtain vectorial representation of images and | |
| recurrent neural networks to decode those representations into natural language | |
| sentences (see Sec.~\ref{section:background}). | |
| \begin{figure}[tp] | |
| \label{figure:model_diagram} | |
| \centering | |
| \caption{Our model learns a words/image alignment. The visualized | |
| attentional maps (3) are explained in section \ref{section:model} \& \ref{section:viz}} | |
| \vspace{3mm} | |
| \includegraphics[width=\columnwidth]{model_diagram.pdf} | |
| \vspace{-6mm} | |
| \end{figure} | |
| \begin{figure*}[!tp] | |
| \label{figure:attention_diagram} | |
| \centering | |
| \caption{Attention over time. As the model generates each word, its attention changes to reflect the relevant parts of the image. ``soft'' | |
| (top row) vs ``hard'' (bottom row) attention. (Note that both models generated the same captions in this example.) } | |
| \includegraphics[width=6.5in]{runout} | |
| \vspace{-5mm} | |
| \end{figure*} | |
| \begin{figure*}[!tp] | |
| \label{figure:alignments} | |
| \centering | |
| \caption{Examples of attending to the correct object (\textit{white} indicates the attended regions, | |
| \textit{underlines} indicated the corresponding word)} | |
| \includegraphics[width=0.87\textwidth]{good.pdf} | |
| \vspace{-5mm} | |
| \end{figure*} | |
| One of the most curious facets of the human visual system is the presence of | |
| attention \citep{Rensink2000,Corbetta2002}. Rather than compress an entire | |
| image into a static representation, attention allows for salient features to | |
| dynamically come to the forefront as needed. This is especially important when | |
| there is a lot of clutter in an image. Using representations (such as those | |
| from the top layer of a convnet) that distill information in image down to the | |
| most salient objects is one effective solution that has been widely adopted in | |
| previous work. Unfortunately, this has one potential drawback of losing | |
| information which could be useful for richer, more descriptive captions. Using | |
| more low-level representation can help preserve this information. However | |
| working with these features necessitates a powerful mechanism to steer the | |
| model to information important to the task at hand. | |
| In this paper, we describe approaches to caption generation that attempt to | |
| incorporate a form of attention with two variants: a ``hard'' attention | |
| mechanism and a ``soft'' attention mechanism. We also show how one advantage of | |
| including attention is the ability to visualize what the model ``sees''. | |
| Encouraged by recent advances in caption generation and inspired by recent | |
| success in employing attention in machine translation \citep{Bahdanau2014} and | |
| object recognition \citep{Ba2014,Mnih2014}, we investigate models that can | |
| attend to salient part of an image while generating its caption. | |
| The contributions of this paper are the following: | |
| \vspace{- 1mm} | |
| \begin{itemize} | |
| \vspace{-2mm} | |
| \item We introduce two attention-based image caption generators under | |
| a common framework (Sec.~\ref{section:model}): 1) a ``soft'' deterministic attention mechanism trainable by standard | |
| back-propagation methods and 2) a ``hard'' stochastic attention mechanism trainable by | |
| maximizing an approximate variational lower bound or equivalently by | |
| REINFORCE~\citep{Williams92}. | |
| \vspace{-2mm} | |
| \item We show how we can gain insight and interpret the results of | |
| this framework by visualizing ``where'' and ``what'' the attention focused on. | |
| (see Sec.~\ref{section:viz}) | |
| \vspace{-2mm} | |
| \item Finally, we quantitatively validate the usefulness of attention in | |
| caption generation with state of the art performance | |
| (Sec.~\ref{section:results}) on three benchmark datasets: Flickr8k | |
| \citep{Hodosh2013} , Flickr30k \citep{Young2014} and the MS COCO dataset | |
| \citep{Lin2014}. | |
| \end{itemize} | |
| \section{Related Work} | |
| \label{section:background} | |
| In this section we provide relevant background on previous work on | |
| image caption generation and attention. | |
| Recently, several methods have been proposed for generating image descriptions. | |
| Many of these methods are based on recurrent neural networks and inspired | |
| by the successful use of sequence to sequence training with neural networks for | |
| machine translation~\citep{Cho2014,Bahdanau2014,Sutskever2014}. One | |
| major reason image caption generation is well suited to the encoder-decoder framework | |
| \citep{Cho2014} of machine translation is because it is analogous | |
| to ``translating'' an image to a sentence. | |
| The first approach to use neural networks for caption generation was | |
| \citet{Kiros2014a}, who proposed a multimodal log-bilinear model that was | |
| biased by features from the image. This work was later followed by | |
| \citet{Kiros2014b} whose method was designed to explicitly allow a natural way | |
| of doing both ranking and generation. \citet{Mao2014} took a similar approach | |
| to generation but replaced a feed-forward neural language model with a | |
| recurrent one. Both \citet{Vinyals2014} and \citet{Donahue2014} use LSTM RNNs | |
| for their models. Unlike \citet{Kiros2014a} and \citet{Mao2014} whose models | |
| see the image at each time step of the output word sequence, | |
| \citet{Vinyals2014} only show the image to the RNN at the beginning. Along | |
| with images, \citet{Donahue2014} also apply LSTMs to videos, allowing | |
| their model to generate video descriptions. | |
| All of these works represent images as a single feature vector from the top | |
| layer of a pre-trained convolutional network. \citet{Karpathy2014} instead | |
| proposed to learn a joint embedding space for ranking and generation whose | |
| model learns to score sentence and image similarity as a function of R-CNN | |
| object detections with outputs of a bidirectional RNN. | |
| \citet{Fang2014} proposed a three-step pipeline for | |
| generation by incorporating | |
| object detections. | |
| Their model first learn detectors for several visual concepts | |
| based on a multi-instance learning framework. A language model trained on | |
| captions was then applied to the detector outputs, followed by rescoring from a | |
| joint image-text embedding space. Unlike these models, our proposed attention | |
| framework does not explicitly use object detectors but instead learns latent | |
| alignments from scratch. This allows our model to go beyond ``objectness'' and | |
| learn to attend to abstract concepts. | |
| Prior to the use of neural networks for generating captions, two main | |
| approaches were dominant. The first involved generating caption templates which | |
| were filled in based on the results of object detections and attribute | |
| discovery (\citet{Kulkarni2013}, \citet{Li2011}, | |
| \citet{Yang2011}, \citet{Mitchell2012}, \citet{Elliott2013}). The second | |
| approach was based on first retrieving similar captioned images from a large | |
| database then modifying these retrieved captions to fit the query | |
| \citep{Kuznetsova2012,Kuznetsova2014}. These approaches typically involved an | |
| intermediate ``generalization'' step to remove the specifics of a caption that | |
| are only relevant to the retrieved image, such as the name of a city. Both of | |
| these approaches have since fallen out of favour to the now dominant neural | |
| network methods. | |
| There has been a long line of previous work incorporating attention into neural | |
| networks for vision related tasks. Some that share the same spirit as our work | |
| include \citet{Larochelle2010,Denil2012,Tang2014}. In particular however, our | |
| work directly extends the work of \citet{Bahdanau2014,Mnih2014,Ba2014}. | |
| \section{Image Caption Generation with Attention Mechanism} | |
| \subsection{Model Details} | |
| \label{section:model} | |
| In this section, we describe the two variants of our attention-based model by | |
| first describing their common framework. The main difference is the definition | |
| of the $\phi$ function which we describe in detail in Section | |
| \ref{sec:det_sto}. We denote vectors with bolded font and matrices with | |
| capital letters. In our description below, we suppress bias terms for | |
| readability. | |
| \subsubsection{Encoder: Convolutional Features} | |
| Our model takes a single raw image and generates a caption $\vy$ | |
| encoded as a sequence of 1-of-$K$ encoded words. | |
| \[ | |
| y = \left\{\vy_1, \ldots, \vy_{C} \right\},\mbox{ } \vy_i \in \RR^{K} | |
| \] | |
| where $K$ is the size of the vocabulary and $C$ is the length of the caption. | |
| We use a convolutional neural network in order to extract a set of feature | |
| vectors which we refer to as annotation vectors. The extractor produces $L$ | |
| vectors, each of which is a D-dimensional representation corresponding to a | |
| part of the image. | |
| \begin{align*} | |
| a = \left\{\va_1, \ldots, \va_L \right\},\mbox{ } \va_i \in \RR^{D} | |
| \end{align*} | |
| In order to obtain a correspondence between the feature vectors and portions of | |
| the 2-D image, we extract features from a lower convolutional layer unlike | |
| previous work which instead used a fully connected layer. This allows the | |
| decoder to selectively focus on certain parts of an image by selecting a subset | |
| of all the feature vectors. | |
| \begin{figure}[tp] | |
| \vskip 0.2in | |
| \begin{center} | |
| \centerline{\includegraphics[width=\columnwidth]{lstm_2.pdf}} | |
| \caption{A LSTM cell, lines with bolded squares imply projections with a | |
| learnt weight vector. Each cell learns how to weigh | |
| its input components (input gate), while learning how to modulate that | |
| contribution to the memory (input modulator). It also learns | |
| weights which erase the memory cell (forget gate), and weights | |
| which control how this memory should be emitted (output gate).} | |
| \label{figure:conditional_lstm} | |
| \end{center} | |
| \vskip -0.3 in | |
| \end{figure} | |
| \subsubsection{Decoder: Long Short-Term Memory Network} | |
| We use a long short-term memory (LSTM) | |
| network~\citep{Hochreiter+Schmidhuber-1997} that produces a caption by | |
| generating one word at every time step conditioned on a context vector, the | |
| previous hidden state and the previously generated words. Our implementation of | |
| LSTM closely follows the one used in \citet{Zaremba2014} (see Fig.~\ref{figure:conditional_lstm}). Using $T_{s,t}: | |
| \RR^{s} \rightarrow \RR^{t}$ to denote a simple affine transformation with | |
| parameters that are learned, | |
| \begin{align} | |
| \label{eq:lstm_gates} | |
| \begin{pmatrix} | |
| \vi_t \\ | |
| \vf_t \\ | |
| \vo_t \\ | |
| \vg_t \\ | |
| \end{pmatrix} = | |
| & | |
| \begin{pmatrix} | |
| \sigmoid \\ | |
| \sigmoid \\ | |
| \sigmoid \\ | |
| \tanh \\ | |
| \end{pmatrix} | |
| T_{D+m+n, n} | |
| \begin{pmatrix} | |
| \vE\vy_{t-1}\\ | |
| \vh_{t-1}\\ | |
| \hat{\vz_t}\\ | |
| \end{pmatrix} | |
| \\ | |
| \label{eq:lstm_memory} | |
| \vc_t &= \vf_t \odot \vc_{t-1} + \vi_t \odot \vg_t \\ | |
| \label{eq:lstm_hidden} | |
| \vh_t &= \vo_t \odot \tanh (\vc_{t}). | |
| \end{align} | |
| Here, $\vi_t$, $\vf_t$, $\vc_t$, $\vo_t$, $\vh_t$ are the input, forget, memory, output | |
| and hidden state of the LSTM, respectively. The vector $\hat{\vz} \in \RR^{D}$ is the context | |
| vector, capturing the visual information associated with a particular | |
| input location, as explained below. $\vE\in\RR^{m\times K}$ is an embedding matrix. Let $m$ and $n$ | |
| denote the embedding and LSTM dimensionality respectively and $\sigma$ and | |
| $\odot$ be the logistic sigmoid activation and element-wise multiplication | |
| respectively. | |
| In simple terms, the context vector $\hat{\vz}_t$ (equations~\eqref{eq:lstm_gates}--\eqref{eq:lstm_hidden}) | |
| is a dynamic representation of the relevant part of the image input at time $t$. | |
| We define a mechanism $\phi$ that computes $\hat{\vz}_t$ from the annotation vectors $\va_i, i=1,\ldots,L$ | |
| corresponding to the features extracted at different image locations. For | |
| each location $i$, the | |
| mechanism generates a positive weight $\alpha_i$ which can | |
| be interpreted either as the probability that location $i$ is the right place to focus | |
| for producing the next word (the ``hard'' but stochastic attention mechanism), | |
| or as the relative importance to give to location $i$ in blending the $a_i$'s together. | |
| The weight $\alpha_i$ of each annotation vector $a_i$ is computed by an | |
| \emph{attention model} $f_{\mbox{att}}$ for which we use a multilayer perceptron conditioned on the previous hidden state $h_{t-1}$. | |
| The soft version of this attention mechanism was introduced by~\citet{Bahdanau2014}. | |
| For emphasis, we note that the hidden state varies as the output RNN advances in | |
| its output sequence: ``where'' the network looks next depends on the sequence of words that has already been | |
| generated. | |
| \begin{align} | |
| e_{ti} =& f_{\mbox{att}} (\va_i, \vh_{t-1}) \\ | |
| \label{eq:alpha} | |
| \alpha_{ti} =& \frac{\exp(e_{ti})}{\sum_{k=1}^L \exp(e_{tk})}. | |
| \end{align} | |
| Once the weights (which sum to one) are computed, the context vector $\hat{z}_t$ | |
| is computed by | |
| \begin{align} | |
| \label{eq:context} | |
| \hat{\vz}_t = \phi\left( \left\{ \va_i \right\}, \left\{ \alpha_i \right\} | |
| \right), | |
| \end{align} | |
| where $\phi$ is a function that returns a single vector given the set of | |
| annotation vectors and their corresponding weights. The details of $\phi$ function | |
| are discussed in Sec.~\ref{sec:det_sto}. | |
| The initial memory state and hidden state of the LSTM are predicted by an | |
| average of the annotation vectors fed through two separate MLPs | |
| ($\text{init,c}$ and $\text{init,h}$): | |
| \begin{align} | |
| \vc_0 = f_{\text{init,c}} (\frac{1}{L} \sum_i^L \va_i) \nonumber \\ | |
| \vh_0 = f_{\text{init,h}} (\frac{1}{L} \sum_i^L \va_i) \nonumber | |
| \end{align} | |
| In this work, we use a deep output layer~\citep{Pascanu2014} to compute the | |
| output word probability given the LSTM state, the context vector and the | |
| previous word: | |
| \begin{gather} | |
| \label{eq:p-out} | |
| p(\vy_t | \va, \vy_1^{t-1}) \propto \exp(\vL_o(\vE\vy_{t-1} + \vL_h\vh_t+ \vL_z \hat{\vz}_t)) | |
| \end{gather} | |
| Where $\vL_o\in\RR^{K\times m}$, $\vL_h\in\RR^{m\times n}$, $\vL_z\in\RR^{m\times D}$, and | |
| $\vE$ are learned parameters initialized randomly. | |
| \section{Learning Stochastic ``Hard'' vs Deterministic ``Soft'' Attention} | |
| \label{sec:det_sto} | |
| In this section we discuss two alternative mechanisms for the attention | |
| model $f_{\mbox{att}}$: stochastic attention and deterministic attention. | |
| \begin{figure*}[!tp] | |
| \label{fig:second} | |
| \caption{Examples of mistakes where we can use attention to gain intuition into what the model saw.} | |
| \includegraphics[width=1.02\textwidth]{errors.pdf} | |
| \label{fig:subfigures} | |
| \end{figure*} | |
| \subsection{Stochastic ``Hard'' Attention} | |
| \label{sec:sto_attn} | |
| We represent the location variable $s_t$ as where the model decides to focus attention | |
| when generating the $t^{th}$ word. $s_{t,i}$ is an indicator one-hot variable which is set to 1 | |
| if the $i$-th location (out of $L$) | |
| is the one used to extract visual features. By treating | |
| the attention locations as intermediate latent variables, we can | |
| assign a multinoulli distribution parametrized by $\{\alpha_i\}$, | |
| and view $\hat{z}_t$ as a random variable: | |
| \begin{align} | |
| \label{eq:s_dist} | |
| p(&s_{t,i} = 1 \mid s_{j<t}, \va ) = \alpha_{t,i} \\ | |
| \label{eq:hard_context} | |
| \hat{\vz}_t &= \sum_i {s}_{t,i}\va_{i}. | |
| \end{align} | |
| We define a new objective function $L_s$ that is a variational lower bound on the marginal log-likelihood | |
| $\log p( \vy \mid \va)$ of observing the sequence of words $\vy$ given image features $\va$. | |
| The learning algorithm for the parameters $W$ of the models can be derived by | |
| directly optimizing $L_s$: | |
| \begin{align} | |
| L_s = & \sum_s p(s \mid \va) \log p(\vy \mid s, \va) \nonumber \\ | |
| \leq & \log \sum_{s}p(s \mid \va)p( \vy \mid s, \va) \nonumber \\ | |
| = & \log p( \vy \mid \va) \label{eq:mll} | |
| \end{align} | |
| \begin{multline} | |
| \label{eq:lb_gradient} | |
| \frac{\partial L_s}{\partial W} = \sum_s p(s \mid \va) \bigg[ \frac{\partial \log p(\vy \mid s, \va)}{\partial W} + \\ | |
| \log p(\vy \mid s, \va) \frac{\partial \log p(s \mid \va)}{\partial W} \bigg]. | |
| \end{multline} | |
| Equation \ref{eq:lb_gradient} suggests a Monte Carlo based sampling | |
| approximation of the gradient with respect to the model parameters. This can be | |
| done by sampling the location $s_t$ from a multinouilli distribution defined by | |
| Equation \ref{eq:s_dist}. | |
| \begin{gather} | |
| \tilde{s_t} \sim \text{Multinoulli}_L(\{\alpha_i\}) \nonumber | |
| \end{gather} | |
| \begin{multline} | |
| \frac{\partial L_s}{\partial W} \approx \frac{1}{N} \sum_{n=1}^{N} \bigg[ \frac{\partial \log p(\vy \mid \tilde{s}^n, \va)}{\partial W} + \\ | |
| \log p(\vy \mid \tilde{s}^n, \va) \frac{\partial \log p(\tilde{s}^n \mid \va)}{\partial W} \bigg] | |
| \end{multline} | |
| A moving average baseline is used to reduce the variance in the | |
| Monte Carlo estimator of the gradient, following~\citet{Weaver+Tao-UAI2001}. | |
| Similar, but more complicated variance | |
| reduction techniques have previously been used by \citet{Mnih2014} and | |
| \citet{Ba2014}. Upon seeing the $k^{th}$ mini-batch, the moving average baseline | |
| is estimated as an accumulated sum of the previous log likelihoods with exponential decay: | |
| \begin{align} | |
| b_k = 0.9 \times b_{k-1} + 0.1 \times \log p(\vy \mid \tilde{s}_k , \va) \nonumber | |
| \end{align} | |
| To further reduce the estimator variance, an entropy term on the multinouilli | |
| distribution $H[s]$ is added. Also, with probability 0.5 for a given image, we | |
| set the sampled attention location $\tilde{s}$ to its expected value $\alpha$. | |
| Both techniques improve the robustness of the stochastic attention learning algorithm. | |
| The final learning rule for the model is then the following: | |
| \begin{multline} | |
| \frac{\partial L_s}{\partial W} \approx \frac{1}{N} \sum_{n=1}^{N} \bigg[ \frac{\partial \log p(\vy \mid \tilde{s}^n, \va)}{\partial W} + \\ | |
| \lambda_r( \log p(\vy \mid \tilde{s}^n, \va) - b) \frac{\partial \log p(\tilde{s}^n \mid \va)}{\partial W} + \lambda_e\frac{\partial H[\tilde{s}^n]}{\partial W} \bigg] | |
| \nonumber | |
| \end{multline} | |
| where, $\lambda_r$ and $\lambda_e$ are two hyper-parameters set by cross-validation. | |
| As pointed out and used in \citet{Ba2014} and \citet{Mnih2014}, this is | |
| formulation is equivalent to the REINFORCE learning rule~\citep{Williams92}, where the | |
| reward for the attention choosing a sequence of actions is a real value proportional to the log likelihood | |
| of the target sentence under the sampled attention trajectory. | |
| In making a hard choice at every point, $\phi\left( \left\{ \va_i \right\}, | |
| \left\{ \alpha_i \right\}\right)$ from Equation \ref{eq:context} is a function that | |
| returns a sampled $\va_i$ at every point in time based upon a multinouilli | |
| distribution parameterized by $\alpha$. | |
| \subsection{Deterministic ``Soft'' Attention} | |
| \label{sec:det_attn} | |
| Learning stochastic attention requires sampling the attention location $s_t$ | |
| each time, instead we can take the expectation of the context vector | |
| $\hat{\vz}_t$ directly, | |
| \begin{align} | |
| \label{eq:s_dist_soft} | |
| \mathbb{E}_{p(s_t|a)}[\hat{\vz}_t] = \sum_{i=1}^L \alpha_{t,i} \va_{i} | |
| \end{align} | |
| and formulate a deterministic attention model by computing a soft | |
| attention weighted annotation vector $\phi\left( \left\{ \va_i \right\}, | |
| \left\{ \alpha_i \right\}\right) = \sum_i^L \alpha_i \va_i$ as introduced | |
| by \citet{Bahdanau2014}. This corresponds to feeding in a | |
| soft $\alpha$ weighted context into the system. The whole model is smooth and | |
| differentiable under the deterministic attention, so learning end-to-end is | |
| trivial by using standard back-propagation. | |
| Learning the deterministic attention can also be understood as approximately | |
| optimizing the marginal likelihood in Equation \ref{eq:mll} under the attention | |
| location random variable $s_t$ from Sec.~\ref{sec:sto_attn}. The hidden activation of | |
| LSTM $\vh_t$ is a linear projection of the stochastic context vector $\hat{\vz}_t$ | |
| followed by $\tanh$ non-linearity. To the first order Taylor approximation, the | |
| expected value $\mathbb{E}_{p(s_t|a)}[\vh_t]$ is equal to computing $\vh_t$ | |
| using a single forward prop with the expected context vector | |
| $\mathbb{E}_{p(s_t|a)}[\hat{\vz}_t]$. Considering Eq.~\ref{eq:p-out}, | |
| let $\vn_t = \vL_o(\vE\vy_{t-1} + \vL_h\vh_t+ \vL_z \hat{\vz}_t)$, | |
| $\vn_{t,i}$ denotes $\vn_t$ computed by setting the random variable $\hat{\vz}$ value to $\va_i$. | |
| We define the normalized weighted geometric mean for the softmax $k^{th}$ word | |
| prediction: | |
| \begin{align} | |
| NWGM[p(y_t=k \mid \va)] &= {\prod_i \exp(n_{t,k,i})^{p(s_{t,i}=1|a)} \over \sum_j \prod_i \exp(n_{t,j,i})^{p(s_{t,i}=1|a)}} \nonumber \\ | |
| &= {\exp(\mathbb{E}_{p(s_{t}|a)}[n_{t,k}]) \over \sum_j \exp(\mathbb{E}_{p(s_t|a)}[n_{t,j}])} \nonumber | |
| \end{align} | |
| The equation above shows the normalized weighted geometric mean of the caption | |
| prediction can be approximated well by using the expected context vector, where | |
| $\mathbb{E}[\vn_t] = \vL_o(\vE\vy_{t-1} + \vL_h\mathbb{E}[\vh_t]+ \vL_z \mathbb{E}[\hat{\vz}_t])$. | |
| It shows that the NWGM of a softmax unit is obtained by applying softmax to the | |
| expectations of the underlying linear projections. Also, from the results in | |
| \cite{baldi2014dropout}, $NWGM[p(y_t=k \mid \va)] \approx \mathbb{E}[p(y_t=k \mid \va)]$ | |
| under softmax activation. That means the expectation of the outputs over all | |
| possible attention locations induced by random variable $s_t$ is computed by | |
| simple feedforward propagation with expected context vector | |
| $\mathbb{E}[\hat{\vz}_t]$. In other words, the deterministic attention model | |
| is an approximation to the marginal likelihood over the attention locations. | |
| \subsubsection{Doubly Stochastic Attention} | |
| \label{section:ds_attn} | |
| By construction, $\sum_i \alpha_{ti} = 1$ as they are the output of a softmax. | |
| In training the deterministic version of our model we introduce a form of | |
| doubly stochastic regularization, where we also encourage $\sum_t \alpha_{ti} \approx | |
| 1$. This can be interpreted as encouraging the model to pay equal attention to | |
| every part of the image over the course of generation. In our experiments, we | |
| observed that this penalty was important quantitatively to improving overall | |
| BLEU score and that qualitatively this leads to more rich and descriptive | |
| captions. In addition, the soft attention model predicts a gating scalar $\beta$ | |
| from previous hidden state $\vh_{t-1}$ at each time step $t$, such that, $\phi\left( \left\{ \va_i \right\}, \left\{ \alpha_i \right\}\right) = \beta \sum_i^L \alpha_i \va_i$, where $\beta_t = \sigma(f_{\beta}(\vh_{t-1}))$. We notice | |
| our attention weights put more emphasis on the objects in the images by | |
| including the scalar $\beta$. | |
| \begin{table*}[!tph] | |
| \label{table:results} | |
| \caption{BLEU-{1,2,3,4}/METEOR metrics compared to other methods, $\dagger$ indicates a different split, (---) indicates an unknown metric, $\circ$ indicates the authors | |
| kindly provided missing metrics by personal communication, $\Sigma$ indicates an ensemble, $a$ indicates using | |
| AlexNet} | |
| \vskip 0.15in | |
| \centering | |
| \begin{tabular}{cccccccc} | |
| \cline{3-6} | |
| & \multicolumn{1}{l|}{} & \multicolumn{4}{c|}{\bf BLEU} & & \\ \cline{1-7} | |
| \multicolumn{1}{|c|}{Dataset} & \multicolumn{1}{c|}{Model} & \multicolumn{1}{c|}{BLEU-1} & \multicolumn{1}{c|}{BLEU-2} & \multicolumn{1}{c|}{BLEU-3} & \multicolumn{1}{c|}{BLEU-4} & \multicolumn{1}{c|}{METEOR} & \\ \cline{1-7} | |
| \multicolumn{1}{|c|}{Flickr8k} & \specialcell{ Google NIC\citep{Vinyals2014}$^\dagger$$^\Sigma$ \\ Log Bilinear \citep{Kiros2014a}$^\circ$ \\ Soft-Attention \\ Hard-Attention } | |
| & \specialcell{63 \\ 65.6 \\ \bf 67 \\ \bf 67 } | |
| & \specialcell{41 \\ 42.4 \\ 44.8 \\ \bf 45.7 } | |
| & \specialcell{27 \\ 27.7 \\ 29.9 \\ \bf 31.4 } | |
| & \specialcell{ --- \\ 17.7 \\ 19.5 \\ \bf 21.3 } | |
| & \multicolumn{1}{c|}{\specialcell{ --- \\ 17.31 \\ 18.93 \\ \bf 20.30 }} \ts\\ | |
| \cline{1-7} | |
| \multicolumn{1}{|c|}{Flickr30k} & \specialcell{ Google NIC$^\dagger$$^\circ$$^\Sigma$ \\Log Bilinear\\ Soft-Attention \\ Hard-Attention } | |
| & \specialcell{66.3 \\ 60.0 \\ 66.7 \\ \bf 66.9 } | |
| & \specialcell{42.3 \\ 38 \\ 43.4 \\ \bf 43.9 } | |
| & \specialcell{27.7 \\ 25.4 \\ 28.8 \\ \bf 29.6 } | |
| & \specialcell{18.3 \\ 17.1 \\ 19.1 \\ \bf 19.9 } | |
| & \multicolumn{1}{c|}{\specialcell{ --- \\ 16.88 \\ \bf 18.49 \\ 18.46}} \ts\\ | |
| \cline{1-7} | |
| \multicolumn{1}{|c|}{COCO} & \specialcell{CMU/MS Research \citep{Chen2014}$^a$ \\ MS Research \citep{Fang2014}$^\dagger$$^a$ \\ BRNN \citep{Karpathy2014}$^\circ$ \\Google NIC$^\dagger$$^\circ$$^\Sigma$ \\ Log Bilinear$^\circ$ \\ Soft-Attention \\ Hard-Attention } | |
| & \specialcell{--- \\ --- \\ 64.2 \\ 66.6 \\ 70.8 \\ 70.7 \\ \bf 71.8 } | |
| & \specialcell{--- \\ --- \\ 45.1 \\ 46.1 \\ 48.9 \\ 49.2 \\ \bf 50.4 } | |
| & \specialcell{--- \\ --- \\ 30.4 \\ 32.9 \\ 34.4 \\ 34.4 \\ \bf 35.7 } | |
| & \specialcell{--- \\ --- \\ 20.3 \\ 24.6 \\ 24.3 \\ 24.3 \\ \bf 25.0 } | |
| & \multicolumn{1}{c|}{\specialcell{ 20.41 \\ 20.71 \\ --- \\ --- \\ 20.03 \\ \bf 23.90 \\ 23.04 }} \ts\\ | |
| \cline{1-7} | |
| \end{tabular} | |
| \vskip -0.1in | |
| \end{table*} | |
| Concretely, the model is trained end-to-end by minimizing the following penalized negative | |
| log-likelihood: | |
| \begin{gather} | |
| L_d = -\log(P(\textbf{y}|\textbf{x})) + \lambda \sum_i^{L}(1 - \sum_t^C \alpha_{ti})^2 | |
| \end{gather} | |
| \subsection{Training Procedure} | |
| Both variants of our attention model were trained with stochastic gradient | |
| descent using adaptive learning rate algorithms. For the Flickr8k dataset, we | |
| found that RMSProp \citep{Tieleman2012} worked best, while for Flickr30k/MS | |
| COCO dataset we used the recently proposed Adam algorithm~\citep{Kingma2014} . | |
| To create the annotations $a_i$ used by our decoder, we used the Oxford VGGnet | |
| \citep{Simonyan14} pre-trained on ImageNet without finetuning. In principle however, | |
| any encoding function could be used. In addition, with enough data, we could also train | |
| the encoder from scratch (or fine-tune) with the rest of the model. | |
| In our experiments we use the 14$\times$14$\times$512 feature map | |
| of the fourth convolutional layer before max pooling. This means our | |
| decoder operates on the flattened 196 $\times$ 512 (i.e $L\times D$) encoding. | |
| As our implementation requires time proportional to the length of the longest | |
| sentence per update, we found training on a random group of captions to be | |
| computationally wasteful. To mitigate this problem, in preprocessing we build a | |
| dictionary mapping the length of a sentence to the corresponding subset of | |
| captions. Then, during training we randomly sample a length and retrieve a | |
| mini-batch of size 64 of that length. We found that this greatly improved | |
| convergence speed with no noticeable diminishment in performance. On our | |
| largest dataset (MS COCO), our soft attention model took less than 3 days to | |
| train on an NVIDIA Titan Black GPU. | |
| In addition to dropout~\citep{Srivastava14}, the only other regularization | |
| strategy we used was early stopping on BLEU score. We observed a breakdown in | |
| correlation between the validation set log-likelihood and BLEU in the later stages | |
| of training during our experiments. Since BLEU is the most commonly reported | |
| metric, we used BLEU on our validation set for model selection. | |
| In our experiments with | |
| soft attention, we also used Whetlab\footnote{\url{https://www.whetlab.com/}} \citep{Snoek2012,Snoek2014} in our | |
| Flickr8k experiments. Some of the intuitions we gained from hyperparameter | |
| regions it explored were especially important in our Flickr30k and COCO experiments. | |
| We make our code for these models based in Theano \citep{Bergstra2010} | |
| publicly available upon publication to encourage future research in this | |
| area. | |
| \section{Experiments} | |
| We describe our experimental methodology and quantitative results which validate the effectiveness | |
| of our model for caption generation. | |
| \subsection{Data} | |
| We report results on the popular Flickr8k and Flickr30k dataset which has 8,000 | |
| and 30,000 images respectively as well as the more challenging Microsoft COCO | |
| dataset which has 82,783 images. The Flickr8k/Flickr30k dataset both come with | |
| 5 reference sentences per image, but for the MS COCO dataset, some of the | |
| images have references in excess of 5 which for consistency across | |
| our datasets we discard. We applied only basic tokenization to MS COCO so that it is | |
| consistent with the tokenization present in Flickr8k and Flickr30k. For all our | |
| experiments, we used a fixed vocabulary size of 10,000. | |
| Results for our attention-based architecture are reported in Table | |
| \ref{table:results}. We report results with the frequently used BLEU | |
| metric\footnote{We verified that our BLEU evaluation code matches the authors of | |
| \citet{Vinyals2014}, \citet{Karpathy2014} and \citet{Kiros2014b}. For fairness, we only compare against | |
| results for which we have verified that our BLEU evaluation code is the same. With | |
| the upcoming release of the COCO evaluation server, we will include comparison | |
| results with all other recent image captioning models.} which is the standard in the | |
| caption generation literature. We report BLEU from 1 to 4 without a brevity | |
| penalty. There has been, however, criticism of BLEU, so in addition we report | |
| another common metric METEOR \citep{Denkowski2014}, and compare whenever | |
| possible. | |
| \subsection{Evaluation Procedures} | |
| A few challenges exist for comparison, which we explain here. The first is a | |
| difference in choice of convolutional feature extractor. For identical decoder | |
| architectures, using more recent architectures such as GoogLeNet or Oxford VGG | |
| \citet{Szegedy2014}, \citet{Simonyan14} can give a boost in performance over | |
| using the AlexNet \citep{Krizhevsky2012}. In our evaluation, we compare directly | |
| only with results which use the comparable GoogLeNet/Oxford VGG features, but | |
| for METEOR comparison we note some results that use AlexNet. | |
| The second challenge is a single model versus ensemble comparison. While | |
| other methods have reported performance boosts by using ensembling, in our | |
| results we report a single model performance. | |
| Finally, there is challenge due to differences between dataset splits. In our | |
| reported results, we use the pre-defined splits of Flickr8k. However, one | |
| challenge for the Flickr30k and COCO datasets is the lack of standardized | |
| splits. As a result, we report with the publicly available | |
| splits\footnote{\url{http://cs.stanford.edu/people/karpathy/deepimagesent/}} | |
| used in previous work \citep{Karpathy2014}. In our experience, differences in | |
| splits do not make a substantial difference in overall performance, but | |
| we note the differences where they exist. | |
| \subsection{Quantitative Analysis} | |
| \label{section:results} | |
| In Table \ref{table:results}, we provide a summary of the experiment validating | |
| the quantitative effectiveness of attention. We obtain state of the art | |
| performance on the Flickr8k, Flickr30k and MS COCO. In addition, we note | |
| that in our experiments we are able to significantly improve the state of the | |
| art performance METEOR on MS COCO that we speculate is connected to | |
| some of the regularization techniques we used \ref{section:ds_attn} and our | |
| lower level representation. Finally, we also note that we are | |
| able to obtain this performance using a single model without an ensemble. | |
| \subsection{Qualitative Analysis: Learning to attend} | |
| \label{section:viz} | |
| By visualizing the attention component learned by the model, we are able to add | |
| an extra layer of interpretability to the output of the model | |
| (see Fig. 1). Other systems that have done this rely on object | |
| detection systems to produce candidate alignment targets~\citep{Karpathy2014}. | |
| Our approach is much more flexible, since the model can attend to ``non | |
| object'' salient regions. | |
| The 19-layer OxfordNet uses stacks of 3x3 filters meaning the only time the | |
| feature maps decrease in size are due to the max pooling layers. The input | |
| image is resized so that the shortest side is 256 dimensional with preserved | |
| aspect ratio. The input to the convolutional network is the center cropped | |
| 224x224 image. Consequently, with 4 max pooling layers we get an output | |
| dimension of the top convolutional layer of 14x14. Thus in order to visualize | |
| the attention weights for the soft model, we simply upsample the weights by a | |
| factor of $2^4$ = 16 and apply a Gaussian filter. We note that the receptive | |
| fields of each of the 14x14 units are highly overlapping. | |
| As we can see in Figure 2 and 3, the model learns alignments | |
| that correspond very strongly with human intuition. Especially in the examples of | |
| mistakes, we see that it is possible to exploit such visualizations to get an | |
| intuition as to why those mistakes were made. We provide a more extensive list of | |
| visualizations in Appendix \ref{section:appendix} for the reader. | |
| \section{Conclusion} | |
| We propose an attention based approach that gives state of the art performance | |
| on three benchmark datasets using the BLEU and METEOR metric. We also show how the | |
| learned attention can be exploited to give more interpretability into the | |
| models generation process, and demonstrate that the learned alignments | |
| correspond very well to human intuition. We hope that the results of this paper | |
| will encourage future work in using visual attention. We also expect that the | |
| modularity of the encoder-decoder approach combined with attention to have | |
| useful applications in other domains. | |
| \section*{Acknowledgments} | |
| \label{sec:ack} | |
| The authors would like to thank the developers of Theano~\citep{Bergstra2010, | |
| Bastien2012}. We acknowledge the support of the following | |
| organizations for research funding and computing support: the Nuance Foundation, NSERC, Samsung, | |
| Calcul Qu\'{e}bec, Compute Canada, the Canada Research Chairs and CIFAR. The authors | |
| would also like to thank Nitish Srivastava for assistance with his ConvNet | |
| package as well as preparing the Oxford convolutional network and Relu Patrascu | |
| for helping with numerous infrastructure related problems. | |
| \small | |
| \bibliography{capgen} | |
| \bibliographystyle{icml2015} | |
| \newpage | |
| \appendix | |
| \normalsize | |
| \onecolumn | |
| \section{Appendix} | |
| \label{section:appendix} | |
| Visualizations from our ``hard'' (a) and ``soft'' (b) attention | |
| model. \textit{White} indicates the regions where the model roughly attends to (see section \ref{section:viz}). | |
| \vskip -0.14in | |
| \begin{figure*}[!ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=6.75in]{61_hard}} | |
| \vskip -0.3in | |
| (a) A man and a woman playing frisbee in a field. | |
| \centerline{\includegraphics[width=6.75in]{61}} | |
| \vskip -0.2in | |
| (b) A woman is throwing a frisbee in a park. | |
| \caption{} | |
| \label{figure:im61} | |
| \vskip -3in | |
| \end{center} | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \vskip 0.2in | |
| \begin{center} | |
| \centerline{\includegraphics[width=7.3in]{1038_hard}} | |
| \vskip -0.2in | |
| (a) A giraffe standing in the field with trees. | |
| \centerline{\includegraphics[width=7.3in]{1038}} | |
| (b) A large white bird standing in a forest. | |
| \caption{} | |
| \label{figure:im1038} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7in]{570_hard}} | |
| \vskip -0.2in | |
| (a) A dog is laying on a bed with a book. | |
| \centerline{\includegraphics[width=7in]{570}} | |
| (b) A dog is standing on a hardwood floor. | |
| \caption{} | |
| \label{figure:im570} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7in]{352_hard}} | |
| \vskip -0.3in | |
| (a) A woman is holding a donut in his hand. | |
| \centerline{\includegraphics[width=7in]{352}} | |
| \vskip -0.4in | |
| (b) A woman holding a clock in her hand. | |
| \caption{} | |
| \label{figure:im352} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \vskip -0.1in | |
| \centerline{\includegraphics[width=8in]{1304_hard}} | |
| \vskip -0.5in | |
| (a) A stop sign with a stop sign on it. | |
| \centerline{\includegraphics[width=9in]{1304}} | |
| \vskip -0.2in | |
| (b) A stop sign is on a road with a mountain in the background. | |
| \caption{} | |
| \label{figure:im1304} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7.5in]{1066_hard}} | |
| \vskip -0.5in | |
| (a) A man in a suit and a hat holding a remote control. | |
| \centerline{\includegraphics[width=7.5in]{1066}} | |
| \vskip -0.2in | |
| (b) A man wearing a hat and a hat on a skateboard. | |
| \caption{} | |
| \label{figure:im1066} | |
| \end{center} | |
| \vskip -0.7in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \vskip 0.2in | |
| \begin{center} | |
| \centerline{\includegraphics[width=7.5in]{120_hard}} | |
| \vskip -0.4in | |
| (a) A little girl sitting on a couch with a teddy bear. | |
| \centerline{\includegraphics[width=7.5in]{120}} | |
| \vskip -0.2in | |
| (b) A little girl sitting on a bed with a teddy bear. | |
| \label{figure:im120} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7in]{3884_hard}} | |
| \vskip -0.4in | |
| (a) A man is standing on a beach with a surfboard. | |
| \centerline{\includegraphics[width=7in]{861_3884}} | |
| \vskip -0.2in | |
| (b) A person is standing on a beach with a surfboard. | |
| \label{figure:im861_3884} | |
| \end{center} | |
| \vskip -0.8in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7.5in]{1322_hard}} | |
| \vskip -0.4in | |
| (a) A man and a woman riding a boat in the water. | |
| \centerline{\includegraphics[width=7.5in]{1322_1463_3861_4468}} | |
| \vskip -0.4in | |
| (b) A group of people sitting on a boat in the water. | |
| \caption{} | |
| \label{figure:im1322_1463_3861_4468} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=8in]{462_hard}} | |
| \vskip -0.5in | |
| (a) A man is standing in a market with a large amount of food. | |
| \centerline{\includegraphics[width=8in]{462_1252}} | |
| \vskip -0.4in | |
| (b) A woman is sitting at a table with a large pizza. | |
| \caption{} | |
| \label{figure:im462_1252} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7.3in]{4837_hard}} | |
| \vskip -0.3in | |
| (a) A giraffe standing in a field with trees. | |
| \centerline{\includegraphics[width=7.3in]{1300_4837}} | |
| \vskip -0.4in | |
| (b) A giraffe standing in a forest with trees in the background. | |
| \caption{} | |
| \label{figure:im1300_4837} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \begin{figure*}[ht] | |
| \begin{center} | |
| \centerline{\includegraphics[width=7.5in]{1228_hard}} | |
| \vskip -0.2in | |
| (a) A group of people standing next to each other. | |
| \centerline{\includegraphics[width=7.5in]{1228}} | |
| \vskip -0.4in | |
| (b) A man is talking on his cell phone while another man watches. | |
| \caption{} | |
| \label{figure:im1228} | |
| \end{center} | |
| \vskip -0.4in | |
| \end{figure*} | |
| \end{document} | |