This repository contains pretrained Show and Tell: A Neural Image Caption Generator implemented in Tensorflow.
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. Finally, given the recent surge of interest in this task, a competition was organized in 2015 using the newly released COCO dataset. We describe and analyze the various improvements we applied to our own baseline and show the resulting performance in the competition, which we won ex-aequo with a team from Microsoft Research, and provide an open source implementation in TensorFlow.READ FULL TEXT VIEW PDF
This repository contains pretrained Show and Tell: A Neural Image Caption Generator implemented in Tensorflow.
A Show And Tell implementation for iOS 11.0 based on CoreML
Machine Learning image to text captions API
show and tell V3
Caption generator for live camera feed
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 . 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 [2, 3]. In contrast, we would like to present in this work a single joint model that takes an image as input, and is trained to maximize the likelihood of producing a target sequence of words where each word comes from a given dictionary, that describes the image adequately.
The main inspiration of our work comes from recent advances in machine translation, where the task is to transform a sentence written in a source language, into its translation in the target language, by maximizing . 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) [4, 5, 6] and still reach state-of-the-art performance. An “encoder” RNN 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 thatgenerates 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. 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. 1). 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. Third, we describe the lessons learned from participating in the first MSCOCO competition, which helped us to improve our initial model and place first in automatic metrics, and first (tied with another team) in human evaluation.
. Traditionally, 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 captioning in natural language has recently enjoyed increased interest. Recent advances in object recognition and detection as well as attribute recognition has been used to drive natural language generation systems, though these are limited in their expressivity. Farhadi et al. use detections to infer a triplet of scene elements which is converted to text using templates. Similarly, Li et al.  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. , but with template-based text generation. More powerful language models based on language parsing have been used as well [11, 12, 13, 14, 15]. 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 [16, 17, 18, 19, 20]. 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  or even image crops and subsentences  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. More recently neural net based recognizer are used to detect a larger set of words and in conjunction with a language model sentences are generated .
In this work we combine deep convolutional nets for image classification  with recurrent networks for sequence modeling , 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 [4, 5, 6], with the difference that instead of starting with a sentence, we provide an image processed by a convolutional net.
In the summer of 2015 a few approaches were introduced which follow the above general paradigm. The closest works are by Kiros et al.  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. [27, 28] 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. Further, Kiros et al.  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. A recurrent network is being used by Donahue et al.  who address in addition activity recognition and video description.
In addition, some approaches try to model in a more explicit fashion the visual anchoring of sentence parts claiming a performance benefit. Xu et al.  explore attention mechanisms over image regions where while emitting words the system can focus on image parts. An explicit word to region alignment is utilized during training by Karpathy et al. . Finally, Chen et al.  build a visual representation for sentence parts while generating the description. Further analysis of the above approaches were reported by Devlin et al. .
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:
where are the parameters of our model, is an image, and its correct transcription. Since
represents any sentence, its length is unbounded. Thus, it is common to apply the chain rule to model the joint probability over, where is the length of this particular example as
where we dropped the dependency on for convenience. At training time, is a training example pair, and we optimize the sum of the log probabilities as described in (2) over the whole training set using stochastic gradient descent (further training details are given in Section 4).
It is natural to model with a Recurrent Neural Network (RNN), where the variable number of words we condition upon up to is expressed by a fixed length hidden state or memory . This memory is updated after seeing a new input by using a non-linear function :
To make the above RNN more concrete two crucial design choices are to be made: what is the exact form of and how are the images and words fed as inputs . For
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 the recent approach of batch normalization and yields the current best performance on the ILSVRC 2014 classification competition35]. The words are represented with an embedding model .
The choice of in (3) is governed by its ability to deal with vanishing and exploding gradients , the most common challenge in designing and training RNNs. To address this challenge, a particular form of recurrent nets, called LSTM, was introduced  and applied with great success to translation [4, 6] and sequence generation .
The core of the LSTM model is a memory cell encoding knowledge at every time step of what inputs have been observed up to this step (see Figure 2) . 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 or zero this value if the gate is . In particular, three gates are being used which control whether to forget the current cell value (forget gate ), if it should read its input (input gate ) and whether to output the new cell value (output gate ). The definition of the gates and cell update and output are as follows:
where represents the product with a gate value, and the various 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 . The nonlinearities are sigmoid and hyperbolic tangent . The last equation
is what is used to feed to a Softmax, which will produce a probability distributionover all words.
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 . 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 of the LSTM at time is fed to the LSTM at time (see Figure 3). All recurrent connections are transformed to feed-forward connections in the unrolled version. In more detail, if we denote by the input image and by a true sentence describing this image, the unrolling procedure reads:
where we represent each word as a one-hot vector of dimension equal to the size of the dictionary. Note that we denote by a special start word and by 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 . The image is only input once, at , 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:
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 .
There are multiple approaches that can be used to generate a sentence given an image, with NIC. The first one is Sampling where we just sample the first word according to , then provide the corresponding embedding as input and sample , continuing like this until we sample the special end-of-sentence token or some maximum length. The second one is BeamSearch: iteratively consider the set of the best sentences up to time as candidates to generate sentences of size , and keep only the resulting best of them. This better approximates . 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. Further experiments on varying the beam size are reported in Section 5.2.5.
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.
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, which asks the graders to evaluate each generated sentence with a scale from 1 to 4111 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
. 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 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 
, which is a form of precision of word n-grams between generated and reference sentences222In 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 4.3. An extensive evaluation protocol, as well as the generated outputs of our system, can be found at 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 (1
)). 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 preferred333Even though it would be more desirable, optimizing for BLEU score yields a discrete optimization problem. In general, perplexity and BLEU scores are fairly correlated..
More recently, a novel metric called CIDER  has been introduced and used by the organizers of the MS COCO Captioning challenge. In a nutshell, it measures consistency between n-gram occurrences in generated and reference sentences, where this consistency is weighted by n-gram saliency and rarity.
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 ). 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.
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:
|Pascal VOC 2008 ||-||-||1000|
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 . 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.
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 4.2, which enabled us to understand our model in depth.
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), and it did help quite a lot in terms of generalization. Another set of weights that could be sensibly initialized are , the word embeddings. We tried initializing them from a large news corpus , 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  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. Further details of the ensambling and additional training improvements used for the MS COCO challenge are described in Section 5.2.
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.
. 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[16, 27, 29], but mostly evaluated in a retrieval framework. A notable exception is , where they did both retrieval and generation, and which yields the best performance on the Flickr datasets up to now.
Human scores in Table II 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 I444We used the implementation of these metrics kindly provided in http://www.mscoco.org.. Despite recent efforts on better evaluation metrics , our model fares strongly versus human raters. However, when evaluating our captions using human raters (see Section 4.3.6), our model fares much more poorly, suggesting more work is needed towards better metrics. For a more detailed description and comparison of our results on the MSCOCO dataset, and other interesting human metrics, see Section 5. In that section, we detail the lessons learned from extra tuning of our model w.r.t. the original model which was submitted in a previous version of this manuscript  (NIC in Table I) versus the latest version for the competition (NICv2 in Table I).
|MNLM 555We computed these BLEU scores with the outputs that the authors of  kindly provided for their OxfordNet system.||56||51|
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 II). 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.
Having trained a generative model that gives , an obvious question is whether the model generates novel captions, and whether the generated captions are both diverse and high quality. Table III 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.
|A man throwing a frisbee in a park.|
|A man holding a frisbee in his hand.|
|A man standing in the grass with a frisbee.|
|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.|
|A display case filled with lots of donuts.|
|A display case filled with lots of cakes.|
|A bakery display case filled with lots of donuts.|
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 IV and V. Note that for the Image Annotation task, we normalized our scores similar to what  used.
|Approach||Image Annotation||Image Search|
Figure 4 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 5. 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.
In order to represent the previous word as input to the decoding LSTM producing , we use word embedding vectors 
, 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. TableVI 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.
|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|
In the spring of 2015, as part of the MS COCO dataset a challenge was organized666More details can be found+ on the competition website: http://mscoco.org/dataset/#captions-challenge2015.. Participants were recommended to train their algorithms on the MS COCO 2014 dataset, and results on the validation and test sets were submitted on an evaluation server, with no more than 5 attempts in total per group, in order to limit overfitting on the test set. Human judges then evaluated the competing approaches and the winners were invited to present their approach at a workshop organized during CVPR 2015.
We entered the competition and the rest of this section explains the various techniques we have explored in this context, building on our baseline model described in the previous sections.
The metrics used are discussed in in Section 4. A special emphasis is on CIDER , which was chosen by the competition organizers to rank teams. As a result we use also during hyper-parameter selection.
|Correlation (vs CIDER)||Human Rank|
We found all the automatic metrics to correlate with each other quite strongly (see Table VII). Notably, the main difference of these metrics is on how humans rank on it versus several automatic image captioning systems (such as the one we propose). Interestingly, BLEU score seems to be quite bad (humans rank 13th out of 16); CIDER fares better (where humans rank 6th); METEOR is the automatic metric where humans rank the highest (third).
In this Section we analyze what components were improved with respect to the model which we originally studied in our CVPR 2015 work . Section 5.3 shows a summary of the results on both automatic and human metrics from the MSCOCO competition. We summarize all the improvements in Table VIII. For reproducibility, we also open source an implementation of our model in TensorFlow 777https://github.com/tensorflow/models/tree/master/im2txt.
|Better Image Model ||2|
|Beam Size Reduction||2|
|Fine-tuning Image Model||1|
|Scheduled Sampling ||1.5|
When we first submitted our image captioning paper to CVPR 2015, we used the best convolutional neural network at the time, known as GoogleLeNet , which had 22 layers, and was the winner of the 2014 ImageNet competition. Later on, an even better approach was proposed in  and included a new method, called Batch Normalization, to better normalize each layer of a neural network with respect to the current batch of examples, so as to be more robust to nonlinearities. The new approach got significant improvement on the ImageNet task (going from 6.67% down to 4.8% top-5 error) and the MSCOCO image captioning task, improving BLEU-4 by 2 points absolute.
In the original set of experiments, to avoid overfitting we initialized the image convolutional network with a pre-trained model (we first used GoogleLeNet, then switched to the better Batch Normalization model), but then fixed its parameters and only trained the LSTM part of the model on the MS COCO training set.
For the competition, we also considered adding some fine tuning of the image model while training the LSTM, which helped the image model focus more on the kind of images provided in the MS COCO training set, and ended up improving the performance on the captioning task.
It is important to note that fine tuning the image model must be carried after the LSTM parameters have settled on a good language model: we found that, when jointly training both, the noise in the initial gradients coming from the LSTM into the image model corrupted the CNN and would never recover. Instead, we train for about 500K steps (freezing the CNN parameters), and then switch to jointly train the model for an additional 100K steps. Training was done using a single GPU (Nvidia K20), and step time was about 3 seconds. Thus, training took over 3 weeks – parallelizing training yielded somewhat worse results, though it increased the speed to convergence.
The improvements achieved by this was 1 BLEU-4 point. More importantly, this change allowed the model to transfer information from the image to the language which was likely not possible due to the insufficient coverage of the ImageNet label space. For instance, after the change we found many examples where we predict the right colors, e.g. “A blue and yellow train …”. It is plausible that the top-layer CNN activations are overtrained on ImageNet-specific classes and could throw away interesting features (such as color), thus the caption generation model may not output words corresponding to those features, without fine tuning the image model.
As explained in Section 3.1, our model uses an LSTM to generate the description given the image. As shown in Figure 3, LSTMs are trained by trying to predict each word of the caption given the current state of the model and the previous word in the caption. At inference, for a new image, the previous word is obviously unknown and is thus replaced by the word generated by the model itself at the previous step. There is thus a discrepancy between training and inference. Recently, we proposed  a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous word, towards a less guided scheme which mostly uses the model generated word instead. We applied this strategy using various schedules for the competition, and found that it improved up to 1.5 BLEU-4 points over using the standard training objective function.
have long been known to be a very simple yet effective way to improve performance of machine learning systems. In the context of deep architectures, one only needs to train separately multiple models on the same task, potentially varying some of the training conditions, and aggregating their answers at inference time. For the competition, we created an ensemble of 5 models trained withScheduled Sampling and 10 models trained with fine-tuning the image model. The resulting model was submitted to the competition, and it further improved our results by 1.5 BLEU-4 points.
In order to generate a sentence with our proposed approach, we described in Section 3.1 the use of BeamSearch, where we maintain a list of the top- sequences of words generated so far. In the original paper, we tried only two values for : 1 (which means only keep the best generated word according to the model at each time step) and 20.
For the competition, we actually tried several more beam sizes, and selected the size which generated the best sequences of words according to the CIDER metric, which we consider to be the metric most aligned with human judgements. Contrary to our expectations, the best beam size turned out to be small: 3.
Note that, as the beam size increases, we score more candidate sentences and pick the best according to the obtained likelihood. Hence, if the model was well trained and the likelihood was aligned with human judgement, increasing the beam size should always yield better sentences. The fact that we obtained the best performance with a relatively small beam size is an indication that either the model has overfitted or the objective function used to train it (likelihood) is not aligned with human judgement.
We also observed that, by reducing the beam size (i.e., with a shallower search over sentences), we increase the novelty of generated sentences. Indeed, instead of generating captions which repeat training captions 80% of the time, this gets reduced to 60%. This hypothesis supports the fact that the model has overfitted to the training set, and we see this reduced beam size technique as another way to regularize (by adding some noise to the inference process).
Reducing the beam size was the single change that improved our CIDER score the most. This simple change yielded more than 2 BLEU-4 points improvement.
All the teams were allowed up to 5 submissions to the evaluation server on a large, unseen set of test images. The leaderboard allowed for teams to monitor progress, and it motivated us to keep improving the accuracy of our model up to the deadline. Despite the automatic metrics not fully characterizing the quality of the captions, strong correlations were present (i.e., improving an automatic metric generally implied a better captioning system).
Since we submitted our paper, and thanks to all the improvements, our BLEU-4 score improved by 8 points absolute (see Section 5.2). The top 5 submission according to the automatic metrics on the test set (sorted by CIDER, and using 5 ground truth captions) are presented in Table IX:
|MSR Captivator ||0.931||0.248||0.526||0.308||2nd|
|m-RNN (2) ||0.886||0.238||0.524||0.302||5th|
The most promising 15 submissions to the MSCOCO challenge, as well as a human baseline, were evaluated on 5 different metrics:
Percentage of captions that are evaluated as better or equal to human caption.
Percentage of captions that pass the Turing Test.
Average correctness of the captions on a scale 1-5 (incorrect - correct).
Average amount of detail of the captions on a scale 1-5 (lack of details - very detailed).
Percentage of captions that are similar to human description.
Note that M1 and M2 were the ones used to decide the winner. The others were merely experimental, but are reported here for completeness.
Results are available on the Leaderboard of the competition website at http://mscoco.org/dataset/#captions-leaderboard. The top 5 submissions according to these metrics (sorted by M1+M2) are shown in Table X:
|MSR Captivator ||0.250||0.301||4.149||2.565||0.233||3rd|
|Berkeley LRCN ||0.246||0.268||3.924||2.786||0.204||5th|
Finally, we show in Figure 6 a few example images together with the caption obtained by our original model, compared with the caption obtained by the final model submitted to the competition. We took a random sample of 20 images from the development set, and picked the ones that looked most interesting (all of them had a better caption except for one). It is clear that the overall quality of the captions have improved significantly, a fact that should be obvious given the overall improvement in BLEU-4 from the improvements that we showed in this section was 8 points absolute.
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. Based on our initial results, we participated in the 2015 MS COCO challenge comparing approaches on the task of image captioning. We presented and analyzed in this paper the various improvements we have made to our basic NIC model and described the competition results which ranked our model in first position using both automatic and human evaluations. 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.
Despite the exciting results on captioning, we believe it is just the beginning. The produced descriptions are one of many possible image interpretations. One possible direction is the have a system which is capable of more targeted descriptions – either anchoring the descriptions to given image properties and locations or being a response to a user specified question or task. Further research direction are better evaluation metrics or evaluation through higher level goals found in application such as robotics.
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. Also many thanks to Chris Shallue for driving the efforts to reimplement and open source our model in TensorFlow.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” inICLR, 2013.