Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: The paper proposes a different approach to instance segmentation using DETR-like
architecture. Instead of the FPN-based approach adopted in the original DETR, they opt for
regressing directly an encoded version of the mask.

This approach allows to perform segmentation in a cheaper manner, and also allows to use the
segmentation objective in the auxiliary loss (not possible in original DETR due to
prohibitive cost), which gives some substantial improvement.

The results shown on Coco are competitive with current state of the art.

main_review: The method is well motivated, and the paper is easy to follow. The ablations thoroughly
explore the technical choices being made (although I have some questions about the said
ablations, see below).

Overall, I find the contribution valuable for the community, since efficient and accurate
instance segmentation is largely unexplored for DETR-like models. I thus lean to accept this
paper. In the rest of this review, I list some questions and possible ways to make the
submission even stronger.

Note that [4] also tackled instance segmentation with a DETR-like architecture (although
mainly focusing of video), and should be discussed in the related work section.

## Comparison with state-of-the-art - SWIN results

I am a bit confused by the presence of the SWIN-L backbone in the main table, without much
explanations.

While it is interesting in theory to combine DETR and SWIN (that creates the first
"transformer only" detector that I am aware of), proper comparison should be made with other
systems. The SWIN paper itself provides numerous detection results, including a 51.1 mask
AP, much better than what is being reported in this paper. It is to be noted that direct
comparison is a bit difficult since this particular result involves numerous bells and whistles. I
do think, however, that a proper comparison, on comparable settings, is advisable for
detection results using this backbone.


## Ablations consistency

From table 3.b, it appears that the optimal coefficient for the $\lambda_\text{vec}$
parameter is 3.0. However, ablations in 3.c, 3.d and 3.e are all conducted with $\lambda_\text{vec}=0.3$

What is the reason behind this? I am not sure what to conclude from ablations performed with
sub-optimal hyper-parameters.

## Timings

Training time could be reported for completeness.

More importantly, I believe it would be valuable to provide inference timings for this
model, compared to the competing ones. One of the major claim of the paper is the simplicity
(one-shot, no ROIalign, ...), but it is not clear that the simplicity actually translates to
faster inference.
In particular, it is not clear to me how costly the DCT decoding actually is.
Similarly, if possible a FLOPS analysis would be interesting.


## Mask quality
Recent papers such as [1] have argued that Mask-IoU doesn't capture the full picture in the
quality of the masks. For example, despite reaching fairly good AP, it is known that
Mask-RCNN masks are sometimes "blobby". It would be interesting to provide more qualitative
comparisons of the performance of the model in various situations. Fig. 4 partially answers
that question by showing failure modes in occluded objects, but what about, for example,
high frequency details in the mask boundaries? (eg the individual fingers of a hand) Would
they be captured adequately? 

Beyond qualitative results, it would also be interesting to provide quantitative metrics,
such as the Boundary-IoU [1], and compare with recent high-quality mask prediction models
such as PointRend [2] and BMask R-CNN[3]

## Segmentation loss in matching?

From the equations provided in section 3.1 and 3.2, it would seem that the segmentation loss
isn't used as part of the matching. Is this correct?
If so, have you also tried incorporating it in the matching cost? Intuitively, it is
desirable to make the loss and the matching as similar as possible, to ensure a smoother
optimization.


## Panoptic segmentation?

In the original DETR paper, one of the main application of the segmentation head was
Panoptic segmentation. How easy would it be to apply this method to this setting as well? In
particular, does the mask encoding work well for stuff segments? Would it be easy to merge
the predictions from different masks? (similar to the pixel-wise argmax done in DETR).



[1] Boundary IoU: Improving Object-Centric Image Segmentation Evaluation, Cheng et al

[2] PointRend: Image segmentation as rendering, Kirillov et al

[3] Boundary-preserving Mask R-CNN, Cheng et al

[4] End-to-End Video Instance Segmentation with Transformers, Wang et al

# Post rebuttal
In light of the rebuttal, I’m increasing my score (6->7)
limitations_and_societal_impact: Nothing stands out.
review_title: null

final_score: rating: 9: Top 15% of accepted NeurIPS papers, strong accept
review_content: summary: The paper proposes and end-to-end instance segmentation approach based on recently proposed Transformer-based object detector DETR. In DETR instance segmentation is done by upsampling of ResNet features from multiple stages, which is computationally expensive. Instead, the authors propose to predict instance masks together with bounding box and class labels, by compressing the masks. This results in a simple and efficient detection and instance segmentation model. Moreover, AP on both tasks improves significantly over DETR and Deformable DETR, achieving very impressive results on par with state-of-the-art, on the challenging COCO dataset. The paper also provides a detailed ablation study of the proposed model.
main_review: Originality: the paper is based on an original and novel idea, predicting compressed instance masks directly with DETR model, avoiding complex operations like proposal generation, non-maximum suppression and ROIAlign, compared to Mask R-CNN.

Quality: the proposed approach to instance segmentation is simple and elegant. The experimental evaluation fully supports the claims, and there is a detailed ablation study for hyperparameter and representation choices. The performance of the proposed approach on COCO benchmarks is very impressive. Similarly to Mask R-CNN Vs Faster R-CNN, adding mask supervision improves bounding box prediction performance.

Clarity: the paper is very well written and is easy to follow. I have only a few questions/suggestions to improve the paper:
- attention visualizations in DETR show that decoder attends to object extremities in order to predict bounding box. It would be interesing to see SOLQ decoder attention visualizations, if it attends to object outline?
- I found "vector loss" confusing, would "Mask vector loss" be a better name?
- would be great to have more details on the Swin-SOLQ experiments, is it "almost" CNN-free object detector?
- I did not find if L_vec loss is also used in cost for bipartite matching?

Significance: the paper greatly improves over DETR and Deformable in detection and segmentation performance, and computational complexity. I expect it to be become a new baseline for both tasks.
limitations_and_societal_impact: The authors addressed the limitations and potential negative societal impact of their work adequately.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: This paper tries to combine DETR and Mask Encoding techniques for end-to-end instance segmentation: DETR could directly separate different objects and Mask Encoding could encode the arbitrary instance mask into a low-dimensional and fix representation. To verify the effectiveness of each component, the authors conducted extensive ablations for each component. The final performance is inspiring, espically using Trasnformer backbone.
main_review: == Pros ==

1. The paper is well motivated and shows a reasonable story. The wring is good and easy to follow.
2. Using Mask Encoding to encode the mask is a good choice for modeling the mask, and the performance (especially AP_s) has been largely improved. 
3. The UQR is better than SQR, and it could further improve the object detection performance.

== Cons == 

1. My main concern is originality. From my understanding, Mask Encoding/Compression has been explored by previous work (i.e., DCT-Mask, MEInst). So adopting it into DETR is straightforward. It could only demonstrate that Mask Encoding works well for DETR-style architecture, but both techniques are not your contribution. 
2. I agree that no previous work has tried combing these techniques together to form an end-to-end instance segmentation system. The performance is also great. But combining weakened your contribution.
3. In L207, the authors claim that DCT-Mask is not learned in an end-to-end manner. However, I believe DCT-Mask is in fact an end-to-end trainable system (please refer to Sec.3 of their paper).
4. SOLQ gets higher AP on small objects, however, it suffers from large objects compared with SOLOv2 (Table 1). Could you explain why?
5. What is the difference between UQR and SQR? From my understanding, UQR is using the encoded mask for supervision, while SQR is using a binary mask. 

== Overall ==

Including Mask Encoding into DETR could directly enable DETR to do instance segmentation. That is good. However, Mask Encoding has been used in the previous two-stage (DCT-Mask), and one-stage (MEInst) methods.  
If you just change the detector from Faster R-CNN/FCOS to DETR, the novelty would be really incremental. It is
important to discuss the differences and highlight the novelty.


limitations_and_societal_impact: n/a
review_title: null

final_score: rating: 3: Clear rejection
review_content: summary: This paper proposes SOLQ, which combines Deformable DETR (D-DETR) and DCT for instance segmentation. The experiments verify the effectiveness of SOLQ to a certain extent.
main_review: - *Originality & Novelty:* **The novelty of SOLQ is limited.** SOLQ studies PCA and DCT for mask representation, which has already been studied in MEInst and DCT-Mask. SOLQ simply verifies DCT for mask representation again on the recently proposed D-DETR.
- *Quality:* 
  - **The motivation of using sparse coding for instance segmentation is not verified in the experiment**. As stated in lines `41-43` of the paper (also in MEInst), the motivation of using sparse coding (e.g., PCA or DCT) for mask representation is that "spatial mask labels results in high computation cost, and sparse coding results in more compact mask representation". But the efficiency of sparse coding (e.g., number of parameters, theoretical FLOPs, and most importantly, FPS) is not studied in the experiments. The paper should provide some quantitative results to explain the reason to use sparse coding for instance segmentation.
  - **The spatial mask representation baseline for instance segmentation (SQR) is relatively weak.** The paper uses Separate Query Representation (SQR), which is originally proposed by DETR, as a kind of spatial mask representation baseline for instance segmentation in D-DETR framework. But SQR is a raw & straightforward design for panoptic segmentation other than instance segmentation, and the results are relatively weak. Since D-DETR / SOLQ has bounding box predictions, I suggest using the box prediction to pool RoIs for mask predictions in an interleaved manner just like HTC as a spatial mask representation baseline. Or the paper can choose to make **fair** comparisons with other arts based on spatial mask representation to verify the effectiveness and efficiency of sparse coding (e.g., PCA or DCT) for mask representation.
  - **The instance segmentation performance of using DCT is not satisfactory**. At first glance the numbers reported in the paper are OK. But the performance gap between $\mathrm{AP}^{box}$ and $\mathrm{AP}^{mask}$ is too **big**, i.e., about $8$ $\mathrm{AP}$, while the gap for the Mask R-CNN family is usually less than $6$ $AP$. This indicates that this kind of mask representation is not satisfactory. The paper claims that SOLQ can improve the object detection performance, so do Mask R-CNN family and many other instance segmentation approaches to my knowledge, since mask annotations can provide additional supervision for object detection. Note that under similar object detection performance, the instance segmentation performance of SOLQ is not competitive. An HTC with $46.3$ $\mathrm{AP}^{box}$ is $1.1$ $\mathrm{AP}^{mask}$ higher than SOLQ with $47.8$ $\mathrm{AP}^{box}$. SOLQ needs to exploit a much better object detector for a reasonable instance segmentation performance. 
  - **Is SOLQ end-to-end?** I think the ultimate objective of building an end-to-end system is to remove the need for many hand-designed components and heuristics. While the use of DCT in SOLQ **introducing two extra hyper-parameters**, i.e., the spatial resolution of ground truth binary mask $N$, and the dimension of mask vector $n_k$. From the ablation study in Table 3 (d) & (e), the performance of SOLQ is **very sensitive to** these two hyper-parameters. Therefore the approach adopted by SOLQ is **clearly inconsistent with** the motivation & goal of designing an end-to-end system. On the other hand, the elimination of NMS is directly inherited from the DETR family, which shouldn't be considered as a contribution of SOLQ.
- Clarity:
  - **This paper makes many unfair comparisons in the experiments.** As a cascade / iterative framework, the qualified competitors for SOLQ are not one-stage MEInst & CondInst & SOLO, etc. But Cascade Mask R-CNN and HTC, etc. However, SOLQ does not make fair comparisons with most approaches in Table 1: SOLQ uses a longer training schedule. 
  - **Figure 1 is very misleading**: the block of "DConv" has a similar size as the Res Blocks, $\mathcal{F}$ & $\mathcal{H}$ (they both have several Transformer layers), which makes the FPN-style instance segmentation design (actually, it is a panoptic segmentation design) of DETR seems to be very heavy. I think the paper should report FPS and compare the efficiency with other arts **quantitatively** other than using these bells and whistles.
  - The experiments are conducted on D-DETR, but in lines `10-12` & lines `72-75` the paper claims they observe improvements on **original DETR**, which is not verified in the experiments. Also, the abstract, introduction & method parts are built on original DETR, while the experiments switch to D-DETR. Therefore many details about the setup of the D-DETR framework are missing.
- Significance: Overall, I believe SOLQ is just yet another instance segmentation model without significant contribution to the object- or instance-level recognition tasks & model design methodology.

limitations_and_societal_impact: N / A
review_title: null

Meta-review: The paper presents a direct way to do instance segmentation in DETR: instead of producing segmentation masks with an FPN, it regresses segmentation masks from compressed representations as queries. This allows training DETR for segmentation end-to-end (while the original DETR and follow-ups have segmentation trained as a second step to detection). 

This paper is roughly the result of the combination of (DF-)DETR with mask encoding (DCT), which is criticized by reviewer 94Kv and f92m. I believe there is nonetheless originality in making it work so well that the approach reaches very strong numbers in segmentation on COCO (39.7 APseg with a ResNet-50 backbone and 45.9 with a Swin-L). 

Reviewer f92m constructively points out that the gap between APbox and APseg is bigger for this approach than for others, which could mean that it requires a stronger detector. Another interpretation can be that training the DF-DETR model with this additional segmentation loss end-to-end boosts the APbox.

Overall, I believe the paper presents a significant contribution, and the authors answered some of the concerns of the reviewers. This is suitable for publication at NeurIPS.
Decision: Accept (Poster)