Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 8: Accept
review_content: This paper is about the task of object detection in the setting of few-shots dataset. The problem is addressed in the learning scheme of meta-learning paradigm: the proposed meta-rcnn trains the popular faster-rcnn on several tasks of few shots object detection while the RPN and the object classification networks are meta-learned among the tasks. Compared to previous work the paper introduces the meta learning framework and several changes to the faster rcnn detector. A prototype representation is derived from the standard RPN network and its proposed bounding box. An attention mechanism choose the object of interest and is used to train the final RPN and classification network. Experiments on the popular Pascal Voc 2007 and ImageNet-FSOD show that the proposed system have state of the art performance.

The paper is very well written, easy to read and of excellent presentation. The introduction of the meta learning paradigm and its use to learn the RPN and classification networks are incremental in novelty but interesting. The experiments are solid and show state of the art performance. As a result I recommend this paper to be accepted.

Minor issues:
- in caption of Fig1: avialable -> available
- in 4.1: “Compared to other variants...” please add a reference to the specific methods you are comparing to.
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: 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).
review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: In this paper, authors propose a meta-learning based approach for low-shot object detection. Specifically, they use prototype in the support set as attention guidance, and learn the category-specific representation for each query image. Subsequently, they use the style of Faster RCNN for object detection.

It is an OK paper with good structure. The idea is somewhat novel, in terms of meta-learning based low-shot detection framework. My main concern is about experiment. First, the data setting is branch new. Why not use the data setting in the literature, e.g., COCO to VOC in LSTD (Chen et al., 2018)? As a result, how to make a fair comparison bothers me a little. Furthermore, LSTD is a non-episodic approach. How to make it in a meta-learning way? Please clarify the implementation details for all other related works in the comparison.  
review_title: Official Blind Review #1

Meta-review: This paper develops a meta-learning approach for few-shot object detection. This paper is borderline and the reviewers are split. The problem is important, albeit somewhat specific to computer vision applications. The main concerns were that it was lacking a head-to-head comparison to RepMet and that it was missing important details (e.g. the image resolution was not clarified, nor was the paper updated to include the details). The authors suggested that the RepMet code was not available, but I was able to find the official code for RepMet via a simple Google search:
https://github.com/jshtok/RepMet
Reviewers also brought up concerns about an ICCV 2019 paper, though this should be considered as concurrent work, as it was not publicly available at the time of submission.
Overall, I think the paper is borderline. Given that many meta-learning papers compare on rather synthetic benchmarks, the study of a more realistic problem setting is refreshing. That said, it's unclear if the insights from this paper would transfer to other machine learning problem settings of interest to the ICLR community.
With all of this in mind, the paper is slightly below the bar for acceptance at ICLR.
Decision: Reject