Venue: ICLR 2017 conference

Reviews:

final_score: rating: 2: Strong rejection
review_content: The authors apply the commonly used Fast RCNN detection system to pedestrian detection. They use “EdgeBoxes” object proposals and incorporate batch norm into their network. Results are shown on the INRIA and ETH pedestrian datasets. They are reasonable but not state-of-the-art. Results are not shown on Caltech Pedestrians, the standard modern dataset used to evaluate pedestrian detection. Perhaps more importantly, the paper has no novelty.

The detection system described in this paper is a standard application of Fast RCNN to pedestrian detection. The implementation is not state-of-the-art, and there is no novelty in this work. EdgeBoxes has been used with Fast RCNN before. The authors don’t seem to be aware of more recent developments in object detection, including Faster RCNN (https://arxiv.org/abs/1506.01497), which uses a deep net to perform object proposals and performs much better than EdgeBoxes, or ResNets (https://arxiv.org/abs/1512.03385) which give a huge boost to Fast RCNN. The authors also propose that using batch norm inside of Fast RCNN is an innovation. It is not, the ResNet Faster RCNN system definitely uses batch norm (as really do most modern implementation of most variants of RCNN). 

While the system described in this paper is reasonable, it is quite dated at this point (judging by the fast progress in object detection). The authors should also see the paper “Is Faster R-CNN Doing Well for Pedestrian Detection?” (https://arxiv.org/abs/1607.07032) which shows a substantially improved result building on Faster RCNN for pedestrian detection. Finally, most recent research on pedestrian detection has been performed on Caltech Peds, that dataset is a must for publishing in this space.

The paper cannot be accepted.
review_title: review

final_score: rating: 3: Clear rejection
review_content: This paper presents experimental results from an EdgeBoxes + Fast R-CNN detector on the task of localizing pedestrians. It uses an AlexNet (CaffeNet) backbone architecture modified to include batch normalization. Experimental results are presented on the INRIA and ETH datasets.

Pros
- The paper is clearly written and easy to follow

Cons
- The paper's two contributions are too minor to merit publication
- Experimental results should include at least the Caltech pedestrian dataset but likely also the KITTI pedestrian dataset
- Recent work from ECCV 2016 [a], with superior results and much more experimental evaluation, is not cited or discussed

My rating is due primarily to the lack luster contributions. The first claimed contribution is the use of EdgeBoxes as proposals for pedestrian detection. Unless the result of this choice produced a truly surprising experimental result, this is simply too minor to be considered a contribution. Moreover, if this choice is important, then the paper should justify it by showing that other proposal methods (of which there are a great many in addition to Selective Search and Edge Boxes) are worse performing in some regard (speed, accuracy, memory, etc.). The second claimed contribution is the use of batch normalization (BN) in their network architecture. There is a case to be made that BN hasn't been explored in Fast R-CNN. However, if the goal of the paper was to thoroughly explore BN + Fast R-CNN, then why focus narrowly on pedestrian detection? Instead, it should focus more broadly on generic object category detection for which there are well established Fast R-CNN baselines on PASCAL VOC and COCO. The use of BN + Fast R-CNN only for pedestrian detection does not provide much signal about this choice. There are also potential technical issues that are not discussed. BN is typically avoided in Fast R-CNN because the batch size seen by most of the network is usually only one or two images. This is likely too few images for the naive application of BN.


[a] "Is Faster R-CNN Doing Well for Pedestrian Detection?" Zhang et al.
review_title: Contributions too weak for ICLR

final_score: rating: 3: Clear rejection
review_content: Paper summary: the authors proposed to use EdgeBoxes + Fast-RCNN with
batch normalization for pedestrian detection

Review summary: results do not cover enough datasets, the reported
results do not improve over state of the art, writing is poor, and
overall the work lacks novelty. This is a clear reject.

Pros:
* Shows that using batch normalization does improve results

Cons:
* Only results on ETH and INRIA. Should include Caltech or KITTI.
* Reported results are fair, but not improving over state of the art
* Overall idea of limited interest when considering works like S.
Zhang CVPR 2016 (Fast R-CNN for pedestrian detection) and L. Zhang
ECCV 2017 (Faster R-CNN for pedestrian detection)
* Issues with the text quality
* Limited takeaways

Quality: low
Clarity: fair, but poor English
Originality: low
Significance: low

For acceptance at future conferences, this work would need more
polish, improving over best known results on INRA, ETH, and Caltech or
KITTI. And ideally, present additional new insights.

Minor comments:
* The text lacks polish. E.g. influent -> influence, has maken ->
made, is usually very important -> is important, achieve more
excellent results -> achieve better results; etc. Please consider
asking help from a native speaker for future submissions. There are
also non-sense sentences such as “it is computational”.
* Citations should be in parentheses
* Some of the citations are incorrect because the family name is in
the wrong position, e.g. Joseph Lim, Lawrence Zitnick, and Rodrigo Benenson.
review_title: Lack of novelty, lack of results

final_score: rating: 3: Clear rejection
review_content: This paper proposes a pedestrian detection method using Fast RCNN framework with batch normalization, where EdgeBoxes is used to collect pedestrian proposals instead of selective search as used in the original Fast RCNN method. The proposed method is evaluated in INRIA and ETH dataset.

Pros:
- The proposed method shows good performance(but not state-of-the-art).

Cons:
- Lack of novelty. Fast RCNN and its variants (e.g. FasterRCNN, https://arxiv.org/abs/1506.01497; Faster RCNN with ResNet, https://arxiv.org/abs/1512.03385) are widely used in object detection tasks in many literatures. Meanwhile, batch normalization is a common practice to train / tune deep networks. It is not new to use batch normalization in CNNs for object / pedestrian detection. 

- The authors claim using EdgeBoxes for pedestrian proposal is another main contribution, and it is interesting that EdgeBoxes (93% recall) performs significantly better than Selective Search (23% recall) on INRIA dataset. It will be better to compare proposal generation method in more details (running time, recall, overlap statistics and number of proposals used), and preferably with more recent methods (RPN in https://arxiv.org/abs/1506.01497, YOLO in https://arxiv.org/abs/1506.02640, and SSD in https://arxiv.org/abs/1512.02325).  

In summary, the contribution of this paper is minor. It cannot be accepted.


review_title: Lack of Novelty

Meta-review: Four knowledgable reviewers recommend rejection due to too weak of a contribution. The authors did not post a rebuttal. The AC agrees with the reviewers' recommendation.
Decision: Reject