Paper:
Despite significant advances in object detection in recent years, training effective detectors in a small data regime remains an open challenge. Labelling training data for object detection is extremely expensive, and there is a need to develop techniques that can generalize well from small amounts of labelled data. We investigate this problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the recently evolving meta-learning principle, we propose a novel meta-learning framework for object detection named "Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data. This learning scheme helps acquire a prior which enables Meta-RCNN to do few-shot detection on novel tasks. Built on top of the Faster RCNN model, in Meta-RCNN, both the Region Proposal Network (RPN) and the object classification branch are meta-learned. The meta-trained RPN learns to provide class-specific proposals, while the object classifier learns to do few-shot classification. The novel loss objectives and learning strategy of Meta-RCNN can be trained in an end-to-end manner. We demonstrate the effectiveness of Meta-RCNN in addressing few-shot detection on Pascal VOC dataset and achieve promising results.

Review:
The paper proposes a method for few-shot object detection (FSOD), a variant of few-shot learning (FSL) where using a support set of few training images for novel categories (usually 1 or 5) not only the correct category labels are predicted on the query images, but also the object instances from the novel categories are localized and their bounding boxes are predicted. The method proposes a network architecture where the sliding window features that enter the RPN are first attenuated using support classes prototypes discovered using (a different?) RPN and found as matching to the few provided box annotations on the support images. The attenuation is by channel wise multiplication of the feature map and concatenation of the resulting feature maps (one per support class). After the RPN, ROI-pooling is applied on the concatenated feature map that is reduced using 1x1 convolution and original feature map (before attenuation) being added to the result. Following this a two FC layer classifier is fine-tuned on the support data to form the final 
RCNN head of the few-shot detector. The whole network is claimed to be meta-trained end to end following COCO or ImageNet (LOC? DET?) pre-training. The method is tested on a split of PASCAL VOC07 into two sets of 10 categories, one for meta-training and the other for meta-testing. In addition, experiments are carried out on ImageNet-LOC animals subset. In both cases, the result are compared to some baselines, and some prior work.
Although FSOD is an important emerging problem, and advances on it are very important, I believe there are still certain gaps in the current paper that need to be fixed before it is accepted. Specifically:
1. Some important details are missing from the description. For example, detectors are usually trained on high resolution images (e.g. 1000 x 1000) and hence are problematic to train with large batches, yet in the proposed approach it is claimed that the proposed model is meta-trained with batch size 5 on 5 way tasks with 10 queries each, so even in 1-shot case, does it mean that 5 x 15 = 75 high resolution images enter the GPU at each batch? I doubt that even in parallel mode with 5 GPUs and 15 high res image per GPU it is possible for claimed backbone architectures (ResNet-50 and VGG16).
As another example, the details of fine-tuning during meta-training seem to be left out, is the model optimized with an inner loop? Details of the RPN that is used to select the support categories prototypes are not specified, where it comes from and how is it trained (clearly as the "main" RPN relies on attenuated features, it cannot be it)? Some additional technical details are not very clear and hinder the reproducibility of the paper (no code seem to be promised?), in general I suggest the authors to improve the writing and clarity of the paper.
2. In VOC07 experiment, FRCN-PN is very vaguely described and being claimed that it stands for RepMet (Karlinksy et al., CVPR 2019). It is not clear what it is and its training procedure on VOC07 is not clearly described.
It is also claimed in ImageNet experiment that the real RepMet is "more carefully designed then FRCN-PN" and has a better backbone, hence it is not clear why FRCN-PN should stand for it.
I suggest the authors to either do a direct comparison or remove their claim of comparison.
3. RepMet paper has proposed an additional benchmark on ImageNet-LOC with 5-way 1/5/10-shot episodes, and afaik it is reproducible as its code is released, so I am wondering as to why it was not used for 
evaluation given that the authors made the effort of reproducing another ImageNet-LOC test on the same categories? It should be evaluated for a fair comparison.
4. Although they don't strictly have to compare to it, I am wondering if the authors would be willing to relate to a similar approach that was proposed for the upcoming ICCV 19: 
"Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning", by Yan et al. Their approach is more similar to RepMet in a sense that the meta-learning is done in the classifier head,
and better results are reported on VOC07 benchmark (and except for 1-shot, higher results are reported for the 3 and 5 shot FRCNN fine-tuning).