Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper is inspired by the human way of labeling object boxes in an image. It proposes a two-stage detector: 

- First, the detector will localize the top-left corner of an object and regress the height and width of the object.
- Then, the detector will refine the box using the cascaded method.

The method is verified on MS-COCO test-dev with multiple backbones, e.g., ResNetXt-101 + deformable convolutional module and SwinTransformer-L.

strengths_and_weaknesses: Strengths:
- Writing is pretty well-formulated and well-organized. It’s easy to follow. 
- The proposed framework belongs to the two-stage one but is different from the conventional two-stage detector. It doesn’t have the region proposal idea in it. Instead, it is developed based on the key-point and grid box regression. The second stage aims to refine the box regression. 
- The experimental results demonstrate that the method is promising with large improvements. 

Weakness:
- Current training strategy for object detection becomes large-scale jittering and fixed input size (1024 x 1024). The training way in the paper is a little outdated. I understand if changing the training method will make comparisons more difficult. So I suggest the paper could use the new training way without comparisons to produce stronger results. This would ease future comparisons in the detection community. 
questions: - YOLO-based methods didn’t show up in the big Table.1. It could be better to add more related top-ranking detectors in it. 
limitations: None.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposes a new object detector based on CornerNet. The proposed detector adopts a two-stage design. In the first stage, instead of detecting both top-left and bottom-right corners, and grouping them by embeddings, the authors propose to detect only the top-left corners and predict the x and y distances between the top-left corners and bottom-right corners to generate an initial set of regions. In the second stage, instead of refining the width and height of the bounding boxes, they propose to refine the corner locations and the distances. The authors argue the this is closer to how human would draw a bounding box. Experiments on COCO show that the proposed detector achieves state-of-the-art results with same backbone.
strengths_and_weaknesses: Strengths:

The proposed approach is interesting and novel, and demonstrates good results on the challenging COCO benchmark. I like the idea of refining corner locations instead of the whole box in the second stage.

Weaknesses:

The main weakness of this paper is that the current experiments do not show sufficiently show that the proposed approach is better than the conventional approach.

The network generates the proposals by predicting the top-left corners, and x and y distances between top-left corners and bottom-right corners. This is different from the corner proposal network [8] or the associative embeddings used in CornerNet. But there is no ablation study that compares these three approaches.

The authors claim that it is easier to learn to detect corners than the centers by showing that the top-left corner model achieve a lower training loss than the center-based model. There are two problems with this claim. First only comparing the difficulties of detecting corners and centers is not sufficient because there are other predictions that are used to generate the final bounding boxes, which may be difficult to predict. Second, training loss is not a good indicator of the final performance so it would be better to compare the models by their validation performance.

In the second stage, the authors propose to refine the corner locations and distances where a conventional object detector would directly refine the center locations. But there are no experiments that support refining corner locations and distances is better and more accurate.

Although the experiments show that the proposed approach outperforms other detectors with the same backbone network, it is not clear where the improvement comes from because the proposed approach uses a cascade network which is known to improve performance while others don’t.
questions: My main concern is that the current experiments do not sufficiently show that the proposed approach is better than the existing approaches. How does regressing distance compare to associative embeddings or corner proposal network in terms of validation performance? How does detecting corners compare to detecting centers in terms of validation performance? How does refining corner location and distances compare to the conventional refining strategy in terms of validation performance? Overall I like the idea of this paper so if the authors provide satisfactory answers to the above questions, I am happy to reconsider my rating.
limitations: N/A
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: In this paper, the authors study the problem of object detection. To be specific, they introduce a new approach for bottom-up detection of objects in images. They take inspiration from how humans label objects (click top-left corner, drag to the bottom-right corner) and propose a two-stage approach mimicking this. They report positive results on the COCO dataset.
strengths_and_weaknesses: Strengths:
+ Taking inspirations from humans in labeling objects is very interesting.

+ A novel mechanism is introduced that mimicks human labeling.


Weaknesses:

- The paper talks about how humans label objects and makes certain assumptions about the process but these are based on beliefs and observations of the authors. One can easily argue that humans might look at/around object centers to determine the top-left corner and then the bottom-right corner. It is not clear whether top-left => bottom-right ordering is affected by right-handedness or cultural differences.

- The paper's improvement over the existing corner-based methods is not clearly demonstrated in the experiments. HumanLiker has a second stage, DCN and therefore extra processing compared to e.g. CornerNet and this can have a huge impact. A more controlled evaluation is needed to justify the contribution of the paper.

- Line 71: "neat and bionic, which makes the model intelligible and friendly to follow." => How do you define and measure these? It is not clear how the proposed approach is related to bionics.

- "2) Instead of single-level feature, we use multi-level features based on FPN [19] to better fit object corners with different size and context;" => Is this really a contribution?

- The proposed mechanism has too many hyper-parameters. Considering the complicated two-stage approach, I wouldn't call this neat or intelligeble. And I would avoid using unnecessarily positive and/but unjustified adjectives such as powerful, bionic, neat, ..

- Fig 4: The difference is so small between the two lines that I am not sure it is safe to make a conclusion from here. With N different random runs, you might obtain different outcomes.


Minor comments:
- Line 39: "for object with peculiar geometry" => "for objects with peculiar geometry".
- Line 158: "which close to the positive" => "which are close to the positive".
- Eq 1: "Objet size" => "Object size". Gk should be defined as a function which takes some input.
- Eq 2: space after "if". "xy" => "x,y".
- Line 164: You should cite on this line CornerNet for Eq 2. The reference on line 167 is insufficient & indirect for the source of Eq 2.
- Eq 2: Please explicitly state that this is class agnostic.
- Line 174: "Due to there" => "Due to the fact that there".
- Line 180: "offset." => "offset:".
- Please read the following guide about writing equations: http://www.ai.mit.edu/courses/6.899/papers/mermin.pdf
- Line 203: "detect box." => "detection box."
- Eq 13: Please use different symbols for these hyper-parameters as you used them before.
- L_ii should be explicitly written in the paper for completeness.
questions: (1) More justifications are require to call this approach human like.

(2) More controlled experiments are required to justify the source of improvement over CornerNet.
limitations: The method is neat, powerful, intelligible, bionic. It doesn't have any limitations.
review_title: null

Meta-review: metareview: This paper received borderline reviews, with one review leaning negative. However, the reviewer acknowledged that their concerns have been addressed but did not update the rating. 

The paper provides an interesting new take on object detection with strong empirical results. The concerns raised by reviewers were mainly about more experimental results and clarifications, which the authors have adequately addressed in their rebuttal. 

For the camera ready version, the authors must change the title of the paper to something more informative. "A human-like object detector" is too vague and non-specific, and misleading given the paper's actual contribution. "human-like" can mean many things (e.g. learning  from very few examples), but the paper is only "human like" in one aspect. In addition, "humanliker" should be replaced by something else ("humanliker" can mean "something that likes human"). 
confidence: Certain
Decision: Accept