Paper:
Modulating early visual processing by language
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic inputs are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the entire visual processing by a linguistic input. Specifically, we introduce Conditional Batch Normalization (CBN) as an efficient mechanism to modulate convolutional feature maps by a linguistic embedding. We apply CBN to a pre-trained Residual Network (ResNet), leading to the MODulatEd ResNet (MODERN) architecture, and show that this significantly improves strong baselines on two visual question answering tasks. Our ablation study confirms that modulating from the early stages of the visual processing is beneficial.

Review:
The paper proposes a novel method called conditional batch normalization (CBN) to be applied on top of existing visual question answering models in order to modulate the visual processing with language information from the question in the early stages. In the proposed method, only the parameters of the batch norm layer of a pre-trained CNN are updated with the VQA loss by conditioning them on the LSTM embedding of the input question.
The paper evaluates the effectiveness of CBN on two VQA datasets – the VQA dataset from Antol et al., ICCV15 and the GuessWhat?! dataset from Vries et al., CVPR17. The experimental results show that CBN helps improve the performance on VQA by significant amount. The paper also studies the effectiveness of adding CBN to different layers and shows that adding CBN to last (top) 2 layers of CNN helps the most. The paper also shows quantitatively that the improvements in VQA performance are not merely due to fine-tuning of CNN by showing that the proposed model performs better than a model in which the Batch Norm parameters are fine-tuned but without conditioning on the language. Hence demonstrating that modulating with language helps. 
Strengths:
1.	The paper is well-motivated and the idea of modulating early visual processing by language is novel and interesting for VQA task. 
2.	The proposed contribution (CBN) can be added on top of any existing VQA model, hence making it widely applicable.
3.	The ablation studies are meaningful and are informative about how much of early modulation by language helps.
4.	The paper provides the details of the hyper-parameters, hence making the work reproducible.
Weaknesses:
1.	The main contribution of the paper is CBN. But the experimental results in the paper are not advancing the state-of-art in VQA (on the VQA dataset which has been out for a while and a lot of advancement has been made on this dataset), perhaps because the VQA model used in the paper on top of which CBN is applied is not the best one out there. But in order to claim that CBN should help even the more powerful VQA models, I would like the authors to conduct experiments on more than one VQA model – favorably the ones which are closer to state-of-art (and whose codes are publicly available) such as MCB (Fukui et al., EMNLP16), HieCoAtt (Lu et al., NIPS16). It could be the case that these more powerful VQA models are already so powerful that the proposed early modulating does not help. So, it is good to know if the proposed conditional batch norm can advance the state-of-art in VQA or not.
2.	L170: it would be good to know how much of performance difference this (using different image sizes and different variations of ResNets) can lead to? 
3.	In table 1, the results on the VQA dataset are reported on the test-dev split. However, as mentioned in the guidelines from the VQA dataset authors (http://www.visualqa.org/vqa_v1_challenge.html), numbers should be reported on test-standard split because one can overfit to test-dev split by uploading multiple entries.
4.	Table 2, applying Conditional Batch Norm to layer 2 in addition to layers 3 and 4 deteriorates performance for GuessWhat?! compared to when CBN is applied to layers 4 and 3 only. Could authors please throw some light on this? Why do they think this might be happening?
5.	Figure 4 visualization: the visualization in figure (a) is from ResNet which is not finetuned at all. So, it is not very surprising to see that there are not clear clusters for answer types. However, the visualization in figure (b) is using ResNet whose batch norm parameters have been finetuned with question information. So, I think a more meaningful comparison of figure (b) would be with the visualization from Ft BN ResNet in figure (a).
6.	The first two bullets about contributions (at the end of the intro) can be combined together.
7.	Other errors/typos:
a.	L14 and 15: repetition of word “imagine”
b.	L42: missing reference
c.	L56: impact -> impacts 
Post-rebuttal comments:
The new results of applying CBN on the MRN model are interesting and convincing that CBN helps fairly developed VQA models as well (the results have not been reported on state-of-art VQA model). So, I would like to recommend acceptance of the paper. 
However I still have few comments --
1. It seems that there is still some confusion about test-standard and test-dev splits of the VQA dataset. In the rebuttal, the authors report the performance of the MCB model to be 62.5% on test-standard split. However, 62.5% seems to be the performance of the MCB model on the test-dev split as per table 1 in the MCB paper (https://arxiv.org/pdf/1606.01847.pdf).
2. The reproduced performance reported on MRN model seems close to that reported in the MRN paper when the model is trained using VQA train + val data. I would like the authors to clarify in the final version if they used train + val or just train to train the MRN and MRN + CBN models. And if train + val is being used, the performance can't be compared with 62.5% of MCB because that is when MCB is trained on train only. When MCB is trained on train + val, the performance is around 64% (table 4 in MCB paper).
3. The citation for the MRN model (in the rebuttal) is incorrect. It should be --
@inproceedings{kim2016multimodal,
  title={Multimodal residual learning for visual qa},
  author={Kim, Jin-Hwa and Lee, Sang-Woo and Kwak, Donghyun and Heo, Min-Oh and Kim, Jeonghee and Ha, Jung-Woo and Zhang, Byoung-Tak},
  booktitle={Advances in Neural Information Processing Systems},
  pages={361--369},
  year={2016}
}
4. As AR2 and AR3, I would be interested in seeing if the findings from ResNet carry over to other CNN architectures such as VGGNet as well.