Title: Compositional Attention Networks for Machine Reasoning
Authors: Drew A. Hudson
Christopher D. Manning
Venue: ICLR 2018
Keywords: Deep Learning
Reasoning
Memory
Attention
VQA
CLEVR
Recurrent Neural Networks
Module Networks
Compositionality

Abstract: We present Compositional Attention Networks, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. While many types of neural networks are effective at learning and generalizing from massive quantities of data, this model moves away from monolithic black-box architectures towards a design that provides a strong prior for iterative reasoning, enabling it to support explainable and structured learning, as well as generalization from a modest amount of data. The model builds on the great success of existing recurrent cells such as LSTMs: It sequences a single recurrent Memory, Attention, and Control (MAC) cell, and by careful design imposes structural constraints on the operation of each cell and the interactions between them, incorporating explicit control and soft attention mechanisms into their interfaces. We demonstrate the model's strength and robustness on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the new model is more computationally efficient, data-efficient, and requires an order of magnitude less time and/or data to achieve good results.

TL;DR: We present a novel architecture, based on dynamic memory, attention and composition for the task of machine reasoning.

Review discussion:
This paper describes a new model architecture for machine reasoning.
In contrast
to previous approaches that explicitly predict a question-specific module
network layout, the current paper introduces a monolithic feedforward network
with iterated rounds of attention and memory. On a few variants of the CLEVR
dataset, it outperforms both discrete modular approaches, existing iterated
attention models, and the conditional-normalization-based FiLM model.
So many models are close to perfect accuracy on the standard CLEVR dataset that
I'm not sure how interesting these results are. In this respect I think the
current paper's results on CLEVR-Humans and smaller fractions of synthetic CLEVR
are much more exciting.
On the whole I think this is a strong paper. I have two main concerns. The
largest is that this paper offers very little in the way of analysis. The model
is structurally quite similar to a stacked attention network or a particular
fixed arrangement of attentive N2NMN modules, and it's not at all clear based on
the limited set of experimental results where the improvements are actually
coming from. It's also possible that many of the proposed changes are
complementary to NMN- or CBN-type models, and it would be nice to know if this
is the case.
Secondarily, the paper asserts that "our architecture can handle
datasets more diverse than CLEVR", but runs no experiments to validate this. It
seems like once all the pieces are in place it should be very easy to get
numbers on VQA or even a more interesting synthetic dataset like NLVR.
Based on a sibling comment, it seems that there may also be some problems with
the comparison to FiLM, and I would like to see this addressed.
On the whole, the results are probably strong enough on their own to justify
admitting this paper.
But I will become much more enthusiastic about if if the
authors can provide results on other datasets (even if they're not
state-of-the-art!) as well as evidence for the following:
1. Does the control mechanism attend to reasonable parts of the sentence?
Here it's probably enough to generate a bunch of examples showing sentence
attentions evolving over time.
2. Do these induce reasonable attentions over regions of the image?
Again, examples are fine.
3. Do the self-attention and gating mechanisms recover the right structure?
In addition to examples, here I think there are some useful qualitative
measures. It should be possible to extract reasonable discretized "reasoning
maps" by running MST or just thesholding on the "edge weights" induced by
attention and gating. Having extracted these from a bunch of examples, you can
compare them to the structural properties of the ground-truth CLEVR network
layouts by plotting a comparison of sizes, branching factors, etc.
4. More on the left side of the dataset size / accuracy curve. What happens if
you only give the model 7000 examples? 700? 70?
Fussy typographical notes:
- This paper makes use of a lot of multi-letter names in mathmode. These are
currently written like $KB$, which looks bad, and should instead be
$\mathit{KB}$.
- Variables with both superscripts and subscripts have the superscripts pushed
off to the right; I think you're writing these like $b_5 ^d$ but they should
just be $b_5^d$ (no space).
- Number equations and then don't bother carrying subscripts like $W_3$, $W_4$
around across different parts of the model---this isn't helpful.
- The superscripts indicating the dimensions of parameter matrices and vectors
are quite helpful, but don't seem to be explained anywhere in the text. I
think the notation $W^{(d \times d)}$ is more standard than $W^{d, d}$.
- Put the cell diagrams right next to the body text that describes them (maybe even
inline, rather than in figures). It's annoying to flip back and forth.
Thanks for the ablations! My score remains the same.
Alright. Thank you very much for your detailed review - it was very helpful to us and we truly appreciate it! We are currently working on Cornell nlvr and also text-based datasets as well as more qualitative experiments and while I believe I shouldn't upload revisions anymore we will definitely explore these directions further!
This paper proposes a recurrent neural network for visual question answering. The recurrent neural network is equipped with a carefully designed recurrent unit called MAC (Memory, Attention and Control) cell, which encourages sequential reasoning by restraining interaction between inputs and its hidden states. The proposed model shows the state-of-the-art performance on CLEVR and CLEVR-Humans dataset, which are standard benchmarks for visual reasoning problem. Additional experiments with limited training data shows the data efficiency of the model, which supports its strong generalization ability.
The proposed model in this paper is designed with reasonable motivations and shows strong experimental results in terms of overall accuracy and the data efficiency.
However, an issue in the writing, usage of external component and lack of experimental justification of the design choices hinder the clear understanding of the proposed model.
An issue in the writing
Overall, the paper is well written and easy to understand, but Section 3.2.3 (The Write Unit) has contradictory statements about their implementation.
Specifically, they proposed three different ways to update the memory (simple update, self attention and memory gate), but it is not clear which method is used in the end.
Usage of external component
The proposed model uses pretrained word vectors called GloVE, which has boosted the performance on visual question answering. This experimental setting makes fair comparison with the previous works difficult as the pre-trained word vectors are not used for the previous works. To isolate the strength of the proposed reasoning module, I ask to provide experiments without pretrained word vectors.
Lack of experimental justification of the design choices
The proposed recurrent unit contains various design choices such as separation of three different units (control unit, read unit and memory unit), attention based input processing and different memory updates stem from different motivations.
However, these design choices are not justified well because there is neither ablation study nor visualization of internal states. Any analysis or empirical study on these design choices is necessary to understand the characteristics of the model. Here, I suggest to provide few visualizations of attention weights and ablation study that could support indispensability of the design choices.
Thank you very much for your review - we truly appreciate it!
We have uploaded a revision (by the rebuttal deadline, jan 5) that addresses all your comments:
1. We have revised the description of the writing unit to make it more clear - we have experimented with several variants for this unit - the "standard" one (for which all the results are about), and 3 variants: a. with self-attention, b. with gating, and c. with both self-attention and gating. In the ablations study section we have included results for each of these for the whole dataset, 10% of the dataset and also showed training curves for each variant.
2. We have trained the models without GloVE and added these results along with clarification to the experiments section.
3. We have included ablation studies in order to justify the architecture design choices and elucidate their impact. We have also added visualizations of attention weights for several examples and discussed them.
Thanks a lot again for your review!
- Paper858 Authors
Summary:
The paper presents a new model called Compositional Attention Networks (CAN) for visual reasoning. The complete model consists of an input unit, a sequence of the proposed Memory, Attention and Composition (MAC) cell, and an output unit. Experiments on CLEVR dataset shows that the proposed model outperforms previous models.
Strengths:
— The idea of building a compositional model for visual reasoning and visual question answering makes a lot of sense, and, I think, is the correct direction to go forward in these fields.
— The proposed model outperforms existing models pushing the state-of-the-art.
— The proposed model is computationally cheaper and generalizes well with less training data as compared to existing models.
— The proposed model has been described in detail in the paper.
Weaknesses:
— Given that the performance of state-on-art on CLEVR dataset is already very high ( <5% error) and the performance numbers of the proposed model are not very far from the previous models, it is very important to report the variance in accuracies along with the mean accuracies to determine if the performance of the proposed model is statistically significantly better than the previous models.
— It is not clear which part of the proposed model leads to how much improvement in performance. Ablations studies are needed to justify the motivations for each of the components of the proposed model.
— Analysis of qualitative results (including attention maps, gate values, etc.) is needed to justify if the model is actually doing what the authors think it should do.
For example, the authors mention an example on page 6 at the end of Section 3.2.2, but do not justify if this is actually what the model is doing.
— Why is it necessary to use both question and memory information to answer the question even when the question was already used to compute the memory information? I would think that including the question information helps in learning the language priors in the dataset. Have the authors looked at some qualitative examples where the model which only uses memory information gives an incorrect answer but adding the question information results in a correct answer?
— Details such as using Glove word embeddings are important and can affect the performance of models significantly.
Therefore, they should be clearly mentioned in the main paper while comparing with other models which do not use them.
— The comparisons of number of epochs required for training and the training time need fixed batch sizes and CPU/GPU configurations. Is that true? These should be reported in this section.
— The authors claim that their model is robust to linguistic variations and diverse vocabulary, by which I am guessing they are referring to experiments on CLEVR-Humans dataset. What is there in the architecture of the proposed model which provides this ability? If it is the Glove vectors, it should be clearly mentioned since any other model using Glove vectors should have this ability.
— On page 6, second paragraph, the authors mention that there are cases which necessitate the model to ignore current memories. Can the authors show some qualitative examples for such cases?
— In the intro, the authors claim that their proposed cell encourages transparency.
But, the design of their cell doesn’t seem to do so, nor it is justified in the paper.
Overall: The performance reported in the paper is impressive and outperforms previous state-of-the-art, but without proper statistical significance analysis of performance, ablation studies, analysis of various attention maps, memory gates, etc. and qualitative results, I am not sure if this work would be directly useful for the research community.
Thanks for the statistical significance analysis, ablation studies, qualitative results and other clarifications, I have increased my rating from 6 to 7. Please fix minor things such as 2 (different) plots for Network Length in Figure 7, short explanation for why the attention is significant on the word "is" instead of an important word "shape" in qualitative examples 1&2 in figure 8, etc.
Also, the current paper length is 14 pages (after the addition of about 4 pages in rebuttal) which is almost double the recommended length of 8 pages, so I would suggest reducing the paper length for future.
Thank you very much for your review and for improving the rating! We will fix the typos and indeed are also working on making the writing a bit more concise to shorten the overall paper length!
Dear reviewers,
Thank you very much for your detailed comments and insightful suggestions for further exploration. We completely agree that ablation studies, statistical significance measures and qualitative analysis such as visualizations are necessary to justify the performance of the model and elucidate its behavior. We are actively working on a revised version of the paper that will include these studies and address the other comments raised in the reviews, in particular regarding the use of GloVE, the model’s performance on smaller subsets (<10%) of CLEVR, and the necessity of predicting the answer using both the question and the memory.
Several clarifications in response to questions from the reviews:
1. For comparative experiments, all the other systems used the original publicly-available authors’ implementations. All the models were trained with an equal batch size of 64 (as in the original implementations) and on the same machine, using a single Titan X Maxwell GPU per model.
2. As mentioned in section 3.2.4, our claims about the model’s robustness to linguistic variability and its ability to handle datasets more diverse than the standard CLEVR indeed refer to its performance on CLEVR-Humans. We believe that attention mechanisms used over the question are a key factor that allows that, as discussed in the supplementary material, section C.4, and supported by Lu et al. (2016) and Yang et al. (2016). We will address this matter further in the revised version of the paper.
Thank you!
- Paper858 Authors
Dear reviewers,
Thank you very much for your detailed suggestions and comments!
We have uploaded a revision that addresses the comments raised in the reviews.
In particular:
- We report the model performance with random initialization for the word vectors rather than GloVE embeddings. Using a uniform initialization, we were able to get equivalent results for CLEVR and 1-2% difference for CLEVR-humans in the new setting, compared to those we have achieved with GloVE.
- We have performed statistical significance analysis, running each model multiple times (10) and updated the plots to show averages and confidence intervals. These experiments show that the results of our model are indeed statistically significant compared to the alternative models.
- We have performed ablation studies that cover the control, reading, and writing units as well as additional aspects of our model. We have shown results for the standard CLEVR dataset and 10% subset of it along with training curves. The ablation study we have conducted quantifies the contribution of each of the model components to its overall performance and shows its relative significance. Based on the results, we can see the importance of using attention over both the question and the image in a coordinated manner through a dual structure of recurrent control and memory paths.
- We have looked into attention maps of the model for the question and image and put a few examples of these in the revised version of the paper that demonstrate the model interpretability.
- For the gating mechanism of the writing unit, we have performed additional experiments showing that untied gate values for each entry of the state vector perform better than having one shared potentially-interpretable gate for the whole state and so have changed the description of that subsection accordingly.
Additional changes in the paper:
- We fixed typos that were in the original submission.
- We have clarified a few missing points that were mentioned in the reviews.
- This includes in particular clarification about the writing unit mods of operation, and the several variants - with self-attention, with a gate, and with both. Each of these variants is accompanied with training curves and final accuracies as part of the ablation section.
In response to specific comments by reviewers:
- The ablations study shows the importance of using both the question and the final memory state. As explained in the revision, since the memory holds information only from the image, it may not contain all required information to answer the question, since potentially crucial aspects of the question are not represented in the image.
For instance: given an image with one object, one question can ask about its size and another question about its color, but in both cases the memory will attend to the same one object and thus will not contain enough information to respond to the question correctly.
- As supported by both the ablations studies and the new experiments that show model's performance on CLEVR-humans without using GloVE, we claim that the model owes its ability to handle diverse language and learn from small amounts of data to the use of attention over question.
In particular, the attention allows the model to ignore varied words in the CLEVR-humans dataset that the model hasn't been necessarily trained on but aren't crucial to understand the question, and rather can focus only on the key words that refer to the objects and their properties.
- As demonstrated by the qualitative results of attention maps over the image and question and explained in the model section, the model is indeed transparent by having access to the attention maps of each computation step. And indeed, examples of such attention maps show interpretable rationales behind the model's predictions.
Thank you very much!
- Paper858 Authors
Updated version of the paper is at: https://arxiv.org/abs/1803.03067
Implementation of the model is at: https://github.com/stanfordnlp/mac-network
See https://cs.stanford.edu/people/dorarad/mac/ for further information and updates!
Thank you!

Meta-review:
role: PC
data: comment: PROS:
1. Good results on CLEVER datasets
2. Writing is clear
3. The MAC unit is novel and interesting.
4. Ablation experiments are helpful

CONS:
The authors overstate the degree to which they are doing "sound" and "transparent" reasoning.  In particular statements such as "Most neural networks are essentially very large correlation engines that will hone in on any statistical, potentially spurious pattern that allows them to model the observed data more accurately. In contrast, we seek to create a model structure that requires combining sound inference steps to solve a problem instance." I think are not supported.  As far as I can tell, the authors' do not show that the steps of these solutions are really doing inference in any sound way

I also found the interpretability section to be a bit unconvincing.  The reviewers and I discussed this and there was some attempt to assess what the operations were actually doing but it is not clear how the language and the image attention are linked.

I wonder whether the learned control activations are abstract and re-used across problems the way that the accompanying functional solution's primitives are.  Have you looked at how similar the controls are across problems which are identical except for a different choice of attributes?  To me, one of the hallmarks of a truly "compositional" solution is one in which the pieces are re-used across problems, not just that there is some sequence of explicit control activations used to solve each individual problem.

Decision: Accept (Poster)