PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Authors: Yuliang Zou
Zizhao Zhang
Han Zhang
Chun-Liang Li
Xiao Bian
Jia-Bin Huang
Tomas Pfister
Venue: ICLR 2021 Poster (2021)
Keywords: pseudo-labeling
semi-supervised
semantic-segmentation

Abstract: Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. Our proposed pseudo-labeling strategy is network structure agnostic to apply in a one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for segmentation. The source code will be released.

Published as a conference paper at ICLR 2021
PSEUDOSEG: DESIGNING PSEUDO LABELS FOR SEMANTIC SEGMENTATION
Yuliang Zou Zizhao Zhang² Han Zhang Chun-Liang Li?
Xiao Bian² Jia-Bin Huang'
Tomas Pfister
Virginia Tech Google Cloud Al GGoolle Brain
ABSTRACT
Recent advances in semi-supervised learning (SSL) demonstrate that combi- nation of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classifica- tion, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to gener- ate well-calibrated structured pseudo labels for training with unlabeled weakly- labeled data. Our proposed pseudo-labeling strategy is network structure agnos- tic apply in one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high- data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for semantic segmentation. The source code is available https://gil http://ggttt/tt//////////.t com goog leinterns /ss..
INTRODUCTION
Image semantic segmentation is a core computer vision task that has been studied for decades. Compared with other vision tasks, such as image classification and object detection, human an- notation of pixel-accurate segmentation is dramatically more expensive. Given sufficient pixel- level labeled training data (i.e., high-data regime), the current state-of-the-art segmentation models (e.g.. DeeeLaav3++ (Chen et al., 2018)) produce satisfactory segmentation prediction for common practical usage. Recent exploration demonstrates improvement over high-data regime settings with large-scale data, including self-training (Chen al., 2020a; Zoph al., 2020) and backbone pre- training (Zhang et al., 2020a).
In contrast to the high-data regime, the performance of segmentation models drop significantly, given very limited pixel-labeled data (i.e., low-data regime). Such ineffectiveness at the low-data regime hinders the applicability of segmentation models. Therefore, instead of improving high-data regime segmentation, our work focuses on data-efficient segmentation training that only relies on few pixel- labeled data and leverages the availability of extra unlabeled or weakly annotated (e.g.. image-level) data to improve performance, with the aim of narrowing the gap to the supervised models trained with fully pixel-labeled data.
Our work is inspired by the recent success in semi-supervised learning (SSL) for image classifica- tion, demonstrating promising performance given very limited labeled data and sufficient amount of unlabeled data. Successful examples include Mean Teacher (Tarvainen & Valpola, 2017), UDA (Xie et al., 2019), MixMatch (Berthelot et al., 2019b), FeatMatch (Kuo et al., 2020), and FixMatch (Sohn al., 2020a). One outstanding idea in this type of SSL is consistency training: making predictions consistent among multiple augmented images. FixMatch (Sohn et al., 2020a) shows that using high-confidence one-hot pscudo labels obtained from augmened unlabeled data to train wwonggl--uugeetee counterpart is the key to the success of SSL. in image classification.
Work done during internship Google Cloud AT Research.

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Published as a conference paper at ICLR 2021
However, effective pseudo labels and well-designed data augmentation are non-trivial to satisfy for semantic segmentation. Although we observe that many related works explore the second condition (i.e.. augmentation) for image segmentation to enable consistency training framework (French et al., 2020; Ouali et al., 2020), we show that a wise design of pseudo labels for segmentation has great veiled potentials.
In this paper, we propose PseudoSeg, a one-stage training framework to improve image semantic segmentation by leveraging additional data either with image-level labels (weakly-labeled data) or without any labels. PseudoSeg presents a novel design of pseudo-labeling to infer effective struc- tured pseudo labels of additional data. It then optimizes the prediction of strongly- aaggeneee data to match its corresponding pseudo labels. In summary, we make the following contributions:
We propose a simple one-stage framework to improve semantic segmentation by using a limited amount of pixel-labeled data and sufficient unlabeled data image-level labeled data. Our framework is simple to apply and therefore network architecture agnostic.
Directly applying consistency training approaches validated in image classification ren- ders particular challenges in segmentation. We first demonstrate how well-calibrated soft pseudo labels obtained through wise fusion of predictions from diverse sources can greatly improve consistency training for segmentation.
We conduct extensive experimental studies on the PASCAL VOC and COCO datasets. Comprehensive analyses are conducted to validate the effectiveness of this method at not only the low-data regime but also the high-data regime. Our experiments study multiple important open questions about transferring SSL advances to segmentation tasks.
2 RELATED WORK
Semi-supervised classification. Semi-supervised learning (SSL) aims to improve model perfor- mance by incorporating a large amount of unlabeled data during training. Consistency regularization and entropy minimization are two common strategies for SSL. The intuition behind consistency- based approaches (I (Laine & Aila, 2016; Sajjadi et al., 2016; Miyato et al., 2018; Tarvainen & Valpola, 2017) is that, the model output should remain unchanged when the input is perturbed. On the other hand, the entropy minimization strategy (Grandvalet & Bengio, 2005) argues that the unlabeled data can be used to ensured classes are well-separated, which can be achieved by encouraging the model to output low-entropy predictions. Pseudo-labeling (Lee, 2013) is one of the methods for implicit entropy minimization. Recently, holistic approaches (Berthelot et al., 2019b;a; Sohn et al., 2020a) combining both strategies have been proposed and achieved significant improvement. By re- designing the pseudo label, we propose an efficient one-stage semi-supervised learning framework of semantic segmentation for consistency training.
Semi-supervised semantic segmentation. Collecting pixel-level annotations for semantic seg- mentation is costly and prone to error. Hence, leveraging unlabeled data in semantic segmentation is natural fit. Early methods utilize GAN-based model either to generate additional training data (Souly et al., 2017) or to learn a discriminator between the prediction and the ground truth mask (Hung et al., 2018; Mittal al., 2019). Consistency regularization based approaches have also been proposed recently, by enforcing the predictions to be consistent, either from augmented input images (French et al., 2020: Kim et al., 2020). perturbed feature embeddings (Ouali et al., 2020), or different networks (Ke et al., 2020). Recently, Luo & Yang (2020) proposes sual-bbanch training network to jointly learn from pixel-accurate and coarse labeled data, achieving good segmentation performance. To push the performance of state of the arts, iterative self-training approaches (Chen et al., 2020a; Zoph et al., 2020; Zhu al., 2020) have been proposed. These methods usually assume the available labeled data is enough to train a good teacher model, which will be used to generate pseudo labels for the student model. However, this condition might not satisfy in the low-data regime. Our proposed method, on the other hand, realizing the ideas of both consistency regular- ization and pseudo-labeling in segmentation, consistently improves the supervised baseline both low-data and high-data regimes.
superved semantic segmentation. Instead of supervising network training with accurate pixel-level labels, many prior works exploit weaker forms of annotations (e.g., bounding boxes (Dai et al., 2015), scribbles (Lin et al., 2016), image-level labels). Most recent approaches use image- level labels as the supervisory signal, which exploits the idea of class activation map (CAM) (Zhou et al., 2016). Since the vanilla CAM only focus on the most discriminative region of objects, dif-
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Self attention Goad (SOC)
(ngiional) Imnee-leeeel
Relinement
Puzado Label
Decoder prodiction
GMdd-CAM
Irpui
prodiction
Calibratod fisien
Werk regrontatio
Seving vegrentatize
Figure I: Overview unlabeled data training branch. Given an image, the weakly augmented version is fed into the network to get the decoder prediction and Self-attention Grad-CAM (SGC). The two sources are then combined via a calibrated fusion strategy to form the pseudo label. The network is trained to make its decoder prediction from strongly augmented image match the pseudo label by a per-pixel cross-entropy loss.
ferent ways to refine CAM have been proposed, including partial image/feature crasing (Hou et al., 2018; Wei et al., 2017: Li et al., 2018), using an additional saliency estimation model (Oh et al., 2017: Huang et al., 2018; 8: Wei et al., 2018), utilizing pixel similarity to propagate the initial score map (Ahn & Kwak, 2018; Wang et al., 2020), mining and co-segment the same category of ob- jects across images (Sun et al., 2020; Zhang et al., 2020b). While achieving promising results using the approaches mentioned above, most of them require multi-stage training strategy. The refined score maps optimized again using dense-CRe CRF model (Krähenbühl & Koltun, 2011), and then used as the target to train a separate segmentation network. On the other hand, we assume there ex- ists small number of fully-annotated data, which allows learn stronger segmentation models than general methods without needing pixel-labeled data.
3 THE PROPOSED METHOD
In analogous to SSL. for classification, our training objective in PseudoSeg consists of a supervised loss L. applied to pixel-level labeled data 'Di, and consistency constraint Lu applied to unlabeled data D., Specifically, the supervised loss L, is the standard pixel-wise cross-entropy loss on the weakly augmented pixel-level labeled examples:
C,
1
Σ
CrossEntropy (vi, fo(w()))),
D
where 0 represents the learnable parameters of the network function f and N denotes the number of valid labeled pixels in image E IR #xxx3 x3, y: RC is the ground truth label of pixel i in x W dimensions, and fx()) E RC is the predicted probability of pixel i, where C the number of classes predict and w(-) denotes the weak (common) data augmentation operations used by Chen et al. (2018).
During training, the proposed PseudoSeg estimates pseudo label H for each strongly- augmented unlabeled data æ D.,, which then used for computing the cross-entropy loss. The unsupervised objective can then be written as:
N-1
C, N
1
Σ
Σ
CrossEntropy (ii, folB (xx))),
(2)
Dal
ZED.
where 8(-) denotes stronger data augmentation operation, which will be described in Section 3.2. We illustrate the unlabeled data training branch in Figure I.
3.1 THE DESIGN OF STRUCTURED PSEUDO LABELS
The next important question is how to generate the desirable pseudo label ÿ. A straightforward solu- tion is directly using the decoder output of a trained segmentation model after confidence threshold-
For simplicity. here illustrate the method with unlabeled data and then show it can be easily adapted to image-level labeled data in Section 3.2.
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ing, suggested by Sohn et al. (2020a); Zoph et al. (2020); Xie et al. (2020); Sohn al. (2020b). However, as we demonstrate later in the experiments, the generated pseudo hard/soft labels as well as other post-processing of outputs are barely satisfactory in the low-data regime, and thus yield inferior final results. To address this issue, our design of pseudo-labeling has two key insights. First, seek for distinct yer efficient decision mechanisms to compensate for the potential errors of decoder outputs. Second, wisely fusing multiple sources of predictions to generate an ensemble and better-calibrated version of pseudo labels.
Starting with localization. Compared with precise segmentation, learning localization is simpler task as it only needs to provide goarser-grained outputs than pixel level of objects in images. Based on this motivation, we improve decoder predictions from the localization perspective. Class activa- tion map (CAM) (Zhou et al., 2016) is a popular approach to provide localization for class-specific regions. CAM-basee methods (Hou al., 2018; Wei et al., 2017: Ahn & Kwak, 2018) have been successfully adopted to tackle different weakly supervised semantic segmentation task from us, where they assume only image-level labels are available. In practice. we adopt a variant of class activation map. Grad-CAM (Selvaraju et al., 2017) in PseudoSeg.
From localization to segmentation. CAM estimates the strength of classifier responses on lo- cal feature maps. Thus, an inherent limitation of CAM-based approaches is that it is prone at- tending only to the most discriminative regions. Although many weakly-supervised segmentation approaches (Ahn & Kwak, 2018; Ahn et al., 2019; Sun et al., 2020) aim at refining CAM local- ization maps to segmentation masks, most of them have complicated post-processing steps, such as dense CRF (Krähenbühl& & Koltun, 201 I), which increases the model complexity when used for con- sistency training. Here we present computationally efficient yet effective refinement alternative, which is learnable using available pixel-labeled data.
Although CAM only localizes partial regions of interests, if we know the pairwise similarities be- tween regions, we can propagate the CAM scores from the discriminative regions to the rest un- attended regions. Actually, it has been shown in many works that the learned high-level deep fea- tures are usually good at similarity measurements of visual objects. In this paper, we find hypercol- umn (Hariharan et al., 2015) with a learnalee similarity measure function works fairly effective.
Given the vanilla -rMd output for all C' classes, which can be viewed as a spatially- -lattenn 2-D vector of weight m TR LxC where each row m, is the response weight per class for one region i. Using a kernel function (C(,;)) :) TR x R. + R that measures element-wise similarity given feature Rd of two regions, the propagated score my E RC can be computed as follows
I-11
m. m4 + Σ
m, W..
j=0 PR= (((Wah.W,h))
The goal of this function is to train + = (WK, W. E RHH We E RCCCC in order to propagate the high value in m to all adjacent clements in the feature space Rd (i.c., hypercolumn features) to region i. Adding m; in equation 3 indicates the skip-connection. To compute propagated score for all regions, the operations in equation 3 can be efficiently implemented with self-attention dot- product (Vaswani et al., 2017). For brevity, we denote this efficient refinement process output as self- attention Grad-CAM (SGC) maps in R " C Figure 6 in Appendix A specifies the architecture.
Calibrated prediction fusion. SGC maps are obtained from low-resolution feature maps. It is then resized to the desired output resolution, and thus not sufficient at delineating crisp boundaries. How- ever, compared to the segmentation decoder, SGC is capable of generating more locally-consisterr masks. Thus, we propose a novel calibrated fusion strategy to take advantage of both decoder and SCG predictions for better pseudo labels.
Specifically, given a batch of decoder outputs (pre-softmax logits) X = fo(((x)) and SGC maps m computed from
data w(x),, generate the pseudo labels ÿ by
m
F(i. m) = Sharpen 7 Softmax
+ (1 (1-7) Softmax (Norm m)
T
(4)
Norm m)
Two critical procedures are proposed to use here to make the fusion process successful. First, p and vi are from different decision mechanisms and they could have very different degrees of
overeonfidence. Therefore, we introduce the operation Norm(a,b) = v V. (a? + +b?) as nor-
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Decoder (strong) Pseudo label Figure 2: Visualization of pseudo labels and other predictions. The generated pseudo label by
Input
Grad-CAM
SGC map
Decoder
fusing the predictions from the decoder and SGC map is used to supervise the decoder (strong) predictions of the strongly-augment
counterpart.
malization factor. It alleviates the over-confident probability after softmax, which could unfavor- ably dominate the resulted 7-averaged probability. Second, the distribution sharpening operation 1/T adjusts the temperature scalar 7 of categorical distribution (Berthelot et al., 2019b; Chen et al.. 2020b). Figure 2 illustrates the predictions from different sources. More importantly, we investigate the aseudo-labeling from a calibration perspective (Sec- tion 4.3), demonstrating that the proposed soft pseudo label leads to a better calibration metric comparing to other possible fusion alternatives, and justifying why benefits the final segmentation performance.
Sharpen(a, 7) 0g" 1/T /Ej (j
Training. Our final training objective contains two extra losses: classification loss Le, and segmentation loss L.a. First, to compute Grad-CAM, we add one-layer classification head after the segmentation backbone and multi-label classification loss L.. Second, as specified in Appendix A (Figure 6), SGC maps are scaled as pixel-wise probabilities using one-layer convolution followed by softmax in equation 3. Learning θ to predict SGC maps needs pixel-labeled data Dj. It is achieved by an extra segmentation loss Lea between SGC maps of pixel-labeled data and corresponding ground truth. All the loss terms are jointly optimized (i.e., Lu + L. + L. + Lea), while Lsa only optimizes & (achieved by stopping gradient). See Figure 7 in the appendix for further details.
3.2 INCORPORATING IMAGE-LEVEL LABELS AND AUGMENTATION
The proposed PseudoSeg can easily incorporate image-level label information (if available) into our one-stage training framework, which also leads consistent improvement as we demonstrate in experiments. We utilize the image-level data with two following steps. First, we directly use ground truth image-level labels generate Grad-CAMs instead of using classifier outputs. Second, they are used to increase classification supervision beyond pixel-level labels for the classifier head.
For strong data augmentation, we simply follow color jittering operations from SimCLR (Chen et al., 2020b) and remove all geometric transformations. The overall strength of augmentation can be controlled by a scalar (studied in experiments). We also apply once random CutOut (DeVries & Taylor, 2017) with region of 50 x 50 pixels since find it gives consistent though minor improvement (pixels inside CutOut regions are ignored in computing losses).
4 EXPERIMENTAL RESULTS
We start by specifying the experimental details. Then, evaluate the method in the settings of using pixel-level labeled data and unlabeled data, as well as using pixel-level labeled data and image-level labeled data, respectively. Next, we conduct various ablation studies to justify our design choices. Lastly, we conduct more comparative experiments in specific settings.
To evaluate the proposed method, we conduct the main experiments and ablation studies on the PAS- CAL VOC 2012 dataset (VOC12) (Everingham et al., 2015), which contains 21 classes including background. The standard VOC12 dataset has 1,449 images as the training set and 1,456 images as the validation set. We randomly subsample 1/2, 1/4, 1/8, and 1/16 of images in the standard training set to construct the pixel-level labeled data. The remaining images in the standard training set, together with the images in the augmented set (Hariharan et al., 2011) (around 9k images), are used as unlabeled or image-level labeled data. To further verify the effectiveness of the proposed method, we also conduct experiments on the COCO dataset (Lin et al., 2014). The COCO dataset has 118,287 images as the training set, and 5,000 images as the validation set. We evaluate on the 80 foreground classes and the background, in the object detection task. As the COCO dataset larger than VOC12, we randomly subsample smaller ratios, 1/32, 1/64, 1/128, 1/256, 1/512, of images from the training set construct the pixel-level labeled data. The remaining images in the
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mlou (%6
mlou
60
Pseudo5eg (Ours)
PseudoSeg (Ours)
Supervised
Supervised
1/8 ins
1722
3/228
1235
1812
Pixel-Ievel labeled data ratio Figure 3: Improvement over the strong supervised baseline, in semi-supervised setting (w/ unlabeled data) on VOC12 val (left) and COCO val (right).
Pixel-evel labeled data ratio
training set are used as unlabeled data or image-level labeled data. evaluate the performance us- ing the standard mean intersection-ovee -overuuuionn (mloU) metric. Implementation details can be found in Appendix B.
4.1 EXPERIMENTS USING PIXEL-ILEVEL LABELED DATA AND UNLABELED DATA
first demonstrate the effectiveness of proposed method by comparing it with the DeepLabv3+ model trained with only the pixel-level labeled data. As shown in Figure (a). the proposed method consistently outperforms the supervised training baseline VOC12, by utilizing the pixel-level labeled data and the unlabeled data. The proposed method not only achieves large performance boost in the low-data regime (when only 6.25% pixel- level labels available), but also improves the performance when the entire training set (1.4k images) available. In Figure: 3 (b), we again observe consistent improvement on the COCO dataset.
Improvement over a strong baseline.
Comparisons with the others. Next, we compare the proposed method with recent state of the arts on both the public 1.4k/9k split (in Table 1) and the created low-data splits (in Table 2), on VOCI2. Our method compares favorably with the others.
Table 1: Comparison with state of the arts on VOC12 val set (w/ pixel-level labeled data and unlabeled data). We use the official training set (1.4k) labeled data, and the augmented set (9k) as unlabeled data.
Method
Network
mloU (%)
GANSeg (Souly et al., 2017)
VGG16
64.10
AdySemSeg (Hung et al., 2018)
ResNet- 101
68.40
CCT (Ouali et al., 2020)
ResNer-50
69.40
PseudoSeg (Ours)
ResNet-50
71.00
PseudoSeg (Ours)
ResNet-101
73.23
Table 2: Comparison with state of the arts VOC12 val set (w/ pixel-level labeled data and unlabeled data) using low-data splits. The exact numbers of pixel-labeled images are shown in brackets. All the methods use ResNet-101 as backbone except CCT (Ouali al., 2020), which uses ResNet-50. indicates implementation from Ke et al. (2020), indicates implementation from French al. (2020).
Method
1/2 (732)
1/4 (366)
1/8 (183)
1/16 (92)
AdySemSeg (Hung ct al., 2018)
65.27
59,97
47.58
39.69
CCT (Ouali et al., 2020)
62.10
58.80
47.60
33.10
*MT (Tarvainen & Valpola, 2017)
69.16
63.01
55.81
48.70
GCT (Ke al., 2020)
70.67
64.71
54.98
46.04
**VAT (Miyato et al., 2018)
63.34
56.88
49.35
36.92
CutMix (French et al., 2020)
69.84
68.36
63.20
55.58
PseudoSeg (Ours)
72.41
69.14
65.50
57.60

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45
73
40
mioU (%)
mlou
35-
PseudoSeg (Ours)
PseudoSeg (Ours)
Supervised
25-
Supervised
1/8 1/66
Pixel-level labeled ratio
Pixel-level labeled data ratio
Figure 4: Improvement over the strong supervised baseline, in a semi-supervised setting (w/ image-level labeled data) on VOCI2 val (left) and COCO val (right).
Table 3: Comparison with state of the arts on VOC12 val
Table 4: Comparison with state of the arts VOC12 val set with pixel-level labeled data and image-level labeled data. Four ratios of pixel-level labeled examples are tested. Both CCT (Ouali al., 2020) and our method use ResNet-50 as backbone.
(w/ pixel-level labeled data and image-level labeled data). We use the official training set (1.4k) as labeled data,
and the augmented set (9k) as image-level labeled data.
Mothod
Model
Network
mloU (%)
WSSN (Papandroou et al., 2015)
DespLab-CRE
VGG16
64.60
GAIN (Li 2018)
DeepLab LFFFL---RF-LFOV
VGG16
60.50
DSRG (Huang al., 2018)
al., 2018)
DeepLaby2
VGG16
64,30
Split
CCT
PseudoSeg
GANSeg (Souly et al,, 2017)
PCN
VGG16
65.80
PickleNet (l.ce al., 2019)
DeepLabv2
ResNer-101
65,80
1/2
66.80
73.51
OCT (Ouali et al., 2020)
PSP-Net
ResNet-50
73.20
1/4
67.60
71.79
PsendoSeg (Ours)
DeepLabyd+
ResNer 50
73,80
1/8
62.50
69.15
1/16
51.80
65.44
4.2 EXPERIMENTS USING PIXEL-LEVEL LABELED DATA AND IMAGE-LEVEL LABELED DATA
Similar to semi-supervised learning using pixel-level labeled data and unlabeled data, we first demonstrate the efficacy of our method by comparing it with a strong supervised bascline. As shown in Figure 4, the proposed method consistently improves the strong baseline on both datasets. In Table 3, we evaluate on the public 1.4k/9k split. The proposed method compares favorably with the other methods. Moreover, we further compare to best compared CCT on the created low-data splits (in Table 4). Both experiments show that the proposed PseudoSeg is more robust than the compared method given less data. On all splits on both datasets, using pixel-level labeled data and image-labelee data shows higher mloU than the setting using pixel-level labeled data and unlabeled data.
4.3 ABLATION STUDY
In this section, we conduct extensive ablation experiments on VOC12 to validate our design choices. How to construct pseudo label? We investigate the effectiveness of the proposed pseudo labeling- Table 5 demonstrates quantitative results, indicating that using either decoder output or SGC alone gives an inferior performance. Naively using decoder output as pseudo labels can hardly work well. The proposed fusion consistently performs better, either with without additional image-level labels. To further answer why our pseudo labels are effective, we study from the model calibration perspective. We measure the expected calibration error (ECE) (Guo et al., 2017) scores of all the intermediate steps and other fusion variants. As shown in Figure 5 (a), the proposed fusion strategy (denoted as G in the figure) achieves the lowest ECE scores, indicating that the significance of jointly using normalization with sharpening (see equation 4) compared with other fusion alternatives. We hypothesize using cell-calibrated soft labels makes model training less affected by label noises. The comprehensive calibration study is left as future exploration direction.
Using hypercolumn feature or not? In Figure 5 (b), we study the effectiveness of using hypercol- umn features instead of the last feature maps in equation 3. We conduct the experiments the 1/16 split of VOC12. As we can see, hypercolumn features substantially improve performance.
Soft or hard pseudo label? How to utilize predictions as pseudo labels remains an active question in SSL. Next, we study whether should use soft or hard one-hot pseudo labels. We conduct
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Table Comparison to alternative pseudo labeling strategies. We conduct experiments using 1/4, 1/8, 1/16 of the pixel-level labeled data, the exact numbers of images are shown in the brackets.
Source
Using image-level labels
1/4 (366)
1/8 (183)
1/16 (92)
Decoder only
70.22
69.35
53.20
SGC only Calibrated fusion
67.07
62.61
53.42
73.79
73.13
67.06
Decoder only
73.95
73.05
67.54
SGC only
/
71.73
67.57
64.26
Calibrated fusion
75.29
74.70
71.22
hypercolumn feature
hysperolumm
46
Norm
mitd (%6
C
F: 4 B Sharper
Gi E Sharpen
miodi
114
94.30
2016
K/rage-ow
Winsceleed
(a) Expected calibration error
(b) Hypercolumn feature
(c) Soft and hard label
76
(8)
mind
Fauztiin (ourh tcaplee45
w/ sharpening (detault] sharpening
Fors (D tocee
2
Seperieed fasltdd)
70
fixe--level labeled data ratio
Pas lave abdied data rade
Pac leve Ubeied data rado
(d) Temperature sharpening
(f) Backbone architecture Figure Ablation studies on different factors. See Section 4.3 for complete details.
(e) Color jittering strength
the experiments in the setting where pixel-level labeled data and image-level labeled data avail- able. As shown Figure 5 (c), using all predictions as soft pseudo label yields better performance than selecting confident predictions. This suggests that well-calibrated soft pseudo labels might be important in segmentation than over-simplified confidence thresholding.
Temperature sharpening or not? We study the effect of temperature sharpening equation 4. We conduct the experiments in the setting where pixel-level labeled data and image-level labeled data are available. As shown in Figure 5 (d), temperature sharpening shows consistent and clear improvements.
Strong augmentation strength. In Figure 5 (e), we study the effects of color jittering in the strong augmentation. The magnitude of jittering strength is controlled by scalar (Chen et al., 2020b). We conduct the experiments in the setting where pixel-level labeled data and unlabeled data are available. If the magnitude is too small, performance drops significantly, suggesting the importance of strong augmentation.
Impact of different feature backbones. In Figure (f), we compare the performance of using ResNet-50, ResNet-101, and Xception-65 as backbone architectures, respectively. We conduct the experiments in the setting where pixel-level labeled data and unlabeled data are available. As we can see, the proposed method consistently improves the baseline by a substantial margin across different backbone architectures.
4.4 COMPARISON WITH SELF-TRAINING
Several recent approaches (Chen et al., 2020a; Zoph al., 2020) exploit the Stucent-Teacher self- training idea to improve the performance with additional unlabeled data. However, these methods only apply self-training in the high-data regime (i.e., sufficient pixel-labeled data to train teachers).
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Table Comparison with self-training. We use our supervised baseline as the teacher generate one-hot pseudo labels, following Zoph et al. (2020).
Method
Using image-level labels
1/4 (366)
1/8 (183)
1/16 (92)
Supervised (Teacher)
70.20
64.00
56.03
Self-training (Student)
72.85
69.88
64.20
PseudoSeg (Ours)
73.79
73.13
67.06
PseudoSeg (Ours)
75.29
74.70
71.22
Here compare these methods in the low-data regimes, where focus on. To generate offline pseudo labels, we closely follow segmentation experiments in Zoph et al. (2020): pixels with a confidence score higher than 0.5 will be used one-hot pseudo labels, while the remaining are treated as ignored regions. This step is considered important to suppress noisy labels. A student model is then trained using the combination of unlabeled data in VOC12 train and augmented sets with generated one-hot pseudo labels and all the available pixel-level labeled data. As shown in Table 6, although the self-training pretty well improves over the supervised baseline, to the proposed method We conjecture that the teacher model usually produces low confidence scores to pixels around boundaries, so pseudo labels of these pixels are filtered in student training- However, boundary pixels are important for improving the performance of segmentation (Kirillov et al., 2020). On the other hand, the design of our method (online soft pseudo labeling process) bypass this challenge. We will conduct more verification of this hypothesis in future work.
inferior
4.5 IMPROVING THE JULLY-SUPERVEE
METHOD WITH ADDITIONAL DATA
We have validated the effectiveness of the proposed method in the low-data regime. In this section, we want to explore whether the proposed method can further improve supervised training in the full training set using additional data. We use the training set (1.4k) in VOC12 as the pixel-level labeled data. The additional data contains additional VOC 9k (VgR), COCO training set (Ctr), and COCO unlabeled data (C,,). More training details can be found in Appendix D. As shown in Table 7, the proposed PseudoSeg is able to improve upon the supervised baseline even in the high-data regime, using additional unlabeled or image-level labeled data.
Table 7: Improving fully supervised model with extra data. No test-time augmentation is used.
Method Baseline
PseudoSeg (w/o image-level labels)
PseudoSeg (w/ senage-level labels)
Extra data
Czr + C. + Voa
Ctr
Ctr + Vox
mloU (%)
76.96
77.40 (+0.44)
78.20 (+1.24)
77.80 (+0.84)
79.28 (+2.32)
DISCUSSION AND CONCLUSION
The key to the good performance of our method in the low-data regime is the novel re-design of pseudo-labeling strategy, which pursues a different decision mechanism from weakly-supervised localization to "remedy" weak predictions from segmentation head. Then augmentation consis- tency training progressively improves segmentation head quality. For the first time, we demon- strate that, with well-calibrated soft pseudo labels, utilizing unlabeled or image-labeled data sig- nificantly improves segmentation at low-data regimes. Further exploration of fusing stronger and better-calibrated pseudo labels worth more study as future directions (e.g., multi-scaling). Although color jittering works within our method as strong data augmentation, we have extensively explored geometric augmentations (leveraging STN (Jaderberg et al., 2015) to align pixels pseudo labels and grongly-augmented predictions) for segmentation but find it not helpful. We believe data aug- mentation needs re-thinking beyond current success in classification for segmentation usage.
ACKNOWLEDGEMEEN
We thank Liang- Chich Chen and Barret Zoph for their valuable comments.
It is difficult directly compare Zoph et al. (2020) in their setting hecause of enormous parallel training, uncommon backbones, and inaccessible pre-training datasets.

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APPENDIX
A SELF-ATTENTION GRAD-CAM
We elaborate the detailed pipeline of generating Self-attention Grad-CAM (SGC) maps (equation 3) in Figure 6. To construct the hypercolumn feature, we extract the feature maps from the last two con- volutional stages of the backbone network and concatenate them together. We then project the hy- percolumn feature to two separate low-dimension embedding spaces to construct "key" and "query", using two 1 convolutional layers. An attention matrix can then be computed via matrix multi- plication of "key" and "query". To construct "value", we compute Grad-CAM for each foreground class and then concatenate them together. This results in H W X (C 1) score map, where the maximum score of each category is normalized to one separately. We then use image-level labels (either from classifier prediction or ground truth annotation) classes to be zero. For each pixel localization, we use one to subtract the maximum score to con- struct the background score map, which is then concatenated with the foreground score maps to form "valuee (H x W x C). The attention score matrix can then be used to reweight and propagate the scores "value". The propagated score is added back to the "value" score map, and the pass through a 1 x 1 convolution (w/ batch normalization) to output the SGC map.
the score maps of non-existing
conv
Score
Self-attention Grad-CAM (SGC)
Hypercelumn feature
soltnax
conv
1x1
ButchNorn
Grad-CAM
Figure 6: Diagram of Self-attention Grad-CAM (SGC).
B IMPLEMENTATION DETAILS
We implement our method on top of the publicly available official DeepLab codebase." 3 Unless spec- ified, we adopt the DeepLabv3+ model with Xception-65 (Chollet, 2017) as the feature backbone, which is pre-trained on the ImageNet dataset (Russakovsky et al., 2015). We train our model follow- ing the default hyper-parameters (e.g., an initial learning rate of 0.007 with polynomial learning rate decay schedule, a crop size of 513 x 513, and an encoder output stride of 16), using GPUs 4 We use batch size of 4 for each GPU for pixel-level labeled data, and 4 for unlabeled/image-let labeled data. For VOC12, we train the model for 30,000 iterations. For COCO, we train the model for 200,000 iterations. We set ? 0.5 and T 0.5 unless specified. We do not apply any test time augmentations.
LOW-DATA SAMPLING IN PASCAL VOC 2012
Unlike random sampling in image classification, is difficult to sample uniformly in a low-data case for semantic segmentation due to the imbalance of rare classes. To avoid the missing classes extremely low data regimes, we repeat the random sampling process for 1/16 three times (while en- suring each class has certain amount) and report the results. We Split in the main manuscript. All splits will be released to encourage reproducibility. The results of all the three splits are shown as in Table 8.
'htpps: / / thub, www/tenson tensor fl www/ncde11 1 reteeeeeeeteeeeeeeee masteersrrrrre rwweercheecccee deep/eeepllllle
We do adopt synchronous hatch normalization, which is known improve performance generally.
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Classifier (L)
4 pixel-level labeled data
4, umlabeled/image-leeee labeled data
Feature backbone
Decoder (L, (L,+L,)
L pixel-level labeled data (converting to image-level) or image-level labeled data
(1,+L +L
L pixel-level labeled data
Sclf-attention (L.
Figure 7: Training. For each network component, show the loss supervision and the corresponding data.
Table 8: Full results 1/16 split in VOC12.
Method
Using image-level labels
Split
Split 2
Split
Supervised
56.03
56.87
55.92
PseudoSeg (Ours)
67.06
64.12
66.09
PseudoSeg (Ours)
71.22
68.
69.72
HIGH-DATE EXPERIMENTAL SETTINGS
Here we provide more details about the experiments in Section 4.5. Since we have a lot more unlabeled/image-evwe labeled data, we adopt a longer training schedule (90,000 iterations) $ We also adopt a slightly different fusion strategy in this setting by using = 0.7 and = 0.3.
E COMPARISON WITH WEAKLY-SUPERVISE APPROACHES
In Table 9, we benchmark recent weakly supervised semantic segmentation performance on PAS- CAL VOC 2012 set. Instead enforcing the consistency between different augmented images we do, these approaches tackle the semantic segmentation task from different perspective, by exploiting the weaker annotations (image-level labels). As we can see, by exploiting the image- level labels with careful designs, weaely-supervised semantic segmentation methods could achieve reasonably well performance. We believe that both perspectives are feasible and promising for low-data regime semantic segmentation tasks, and complementary each other. Therefore, these designs could be potentially integrated into our framework to generate better pseudo labels, which leads to improved performance.
Table 9: Benchmarking state-of-the-art weakly supervised semantic segmentation methods.
All the methods use image-level labels from VOC12 training (1.4k) and augmented (9k) sets.
Method
Pixel-level labeled data
mloU (%)
FickleNet (Lee al., 2019)
64.9
IRNet (Ahn et al., 2019)
63.5
OAA+ (Jiang al., 2019)
65.2
SEAM (Wang et al., 2020)
64.5
MCIS (Sun et al., 2020)
66.2
PseudoSeg (Ours)
1/16 (92)
71.22
Note that longer training schedule does improve the supervised baseline.
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F PERFORMANCE ANALYSIS FOR TEMPERATURE SHARPENING
We conduct an additional performance analysis for temporal sharpening. We conduct experiments over T on the 1/16 split of VOC using pixel-level labeled data and image-level labeled data. As shown in Table 10, adopting a T < 1 for distribution sharpening generally leads to improved per- formance.
Table 10: Performance analysis over
Temperature (T)
mloU (%)
0.1
71.11
0.3
70.11
0.5 (default)
71.22
0.7
72.37
1.0 (no sharpening)
68.15
G EXPERIMENTS ON CITYSCAPES
In this section, we conduct additional experiments on the Cityscapes dataset (Cordts et al., 2016). The Cityscapes dataset contains 50 real-world driving sequences. Among these video sequences, 2,975 frames are selected as the training set, and 500 frames are selected as the validation set. Following previous common practice, we evaluate on 19 semantic classes.
Comparison with state of the art. We compare our method with the current state-of-the-art method (French et al., 2020), in the setting of using pixel-level labeled and unlabeled data. We ran- domly subsample 1/4, 1/8, and 1/30 of the training set to construct the pixel-level labeled data, using the first random seed provided by French al. (2020). Both French et al. (2020) and method use ResNet-101 as the feature backbone and DeepLabv3+ (Chen et al., 2018) as the segmentation model. As shown in Table 11, the proposed method achieves promising results on all the three label ratios.
Table 11: Experiments on Cityscapes (w/ pixel-level labeled data and unlabeled data).
Method
1/4 (744)
1/8 (372)
1/30 (100)
CutMix (French et al., 2020)
68.33
65.82
55.71
PseudoSeg (Ours)
72.36
69.81
60.96
Per-class performance analysis. Next, we provide per-class performance break down analysis. We compare our method with the supervised baseline on the 1/30 split, using pixel-level labeled data and unlabeled data. As shown in Table 12, the distribution of the labeled pixels is severely imbalanced. Although our method does not in particular address the data imbalance issue, our method improves upon the supervised baseline on most of the classes (except for "Wall" and "Pole").
Table 12: Per-class performance analysis on Cityscapes (w/ pixel-level labeled data and unlabeled data).
Class
Road
Sidewalk 5.61
Building
Wall
Fence
Pole
Traffic light
Traffic sign
Vegetation 19.61
Terrain
Pixel ratio (%)
36.36
20.99
0.53
0.98
1.19
0.14
0.51
1.29
Supervised
96.03
71.26
87.53
19.75
29.11
52.19
50.19
68.09
89.93
45.79
PseudoSog (Ours)
96.64
75.06
88.63
19.67
34.09
51.75
58.19
69.95
90.43
50.48
Class
Sky
Person
Rider
Truck
Bus
Train
Motorcycle 0.06
Bicycle 0.54
Pixel ratio (%)
3,70
1.10
0.16
6,49
0.38
0.13
0,23
Supervised
91.01
72
43.91
89 .1
7.68
14.19
17.78
25.86
69.88
PseudoSeg (Ours)
92.99
75.16
26.30
22.13
Discussion. Although the scene layouts are quite similar for all the full images, it is still feasible to generate different image-level labels through a more aggressive geometric data augmentation (e.g.. scaling, cropping, translation, etc.). In practice, standard segmentation preprocessing steps only crop sub-region of the whole training images. It only contains partial images with certain subset of image labels, making the training batches have diverse image-level labels (converted from pixel- level labels, in the hully-labeled-unlbbellee setting). Moreover, in the fullylahheedd-waakyy-aaaat
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setting, in practice, collect diverse Internet images and weakly label them, instead of weakly labeling images from Cityscapes.
QUALITATIVE RESULTS
We visualize several model prediction results for PASCAL VOC 2012 (Figure 8) and COCO (Fig- ure 9). As we can see, the supervised baseline struggles to segment some of the categories and small objects, when trained in the low-data regime. On the other hand, PseudoSeg utilizes unlabeled or weakly-labeled data to generate more satisfying predictions.
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Input
Ground truth Supervised
Ours (unlabeled) Ours (img. label)
Figure 8: Qualitative results PASCAL VOC 2012. Models are trained with 1/16 pixel-level labeled data in the training set.
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Input
Ground truth
Supervised
Ours (unlabeled) Ours (img. label)
Figure 9: Qualitative results COCO. Models are trained with 1/512 pixel-level labeled data the training set. Note that white pixel in the ground truth indicates this pixel is not annotated for evaluation.
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Reviews:
[{"review_id": "wf54ocXt0c", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "** Summary\nThis work addresses the task of semi-supervised learning (SSL) in semantic segmentation. Following recent SOTAs in SSL, this work also advocates for the use of pseudo-labels on unlabeled data and heavy data augmentation. The main novelty of this work is the novel way to construct higher-quality pseudo-labels: besides the pixel-wise classifier's probabilistic outputs, the authors leverage as well CAM-based activation maps, named as SGC, as an additional pseudo-label source.  The final set of pseudo-labels is determined by linear combining the two soft pseudo-label sources with temperature adjustment. The authors conducted extensive experiments with lots of ablation studies to validate the proposed framework.\n\n** Strengths:\n- The paper is well-written, easy to follow\n- Extensive experiments with adequate discussions\n- Improvements over SOTAs on the addressed benchmarks.\n\n** Weakness/concerns:\n - Does \"distribution sharpen operation\" always use temperature < 1? If yes, what is the reason?\n - How is the temperature $T$ is chosen? May the authors produce a performance analysis over T?\n - In Sec 3.4, it's not clear to me the advantage of proposed method on boundaries. CAM-based activations mostly focus on most discriminative areas (usually inner areas). So hardly SGC can find pseudo-labels on boundaries. Why does the proposed method have an advantage there?\n - More and more segmentation works report results in urban datasets like cityscapes or camvid. It would be interesting to see results on those datasets. One interesting aspect in urban datasets is the natural long-tail class distributions, which severely damages performance on minor classes, especially in low-data regime.", "extra_scores": {}, "extra_text": {}}, {"review_id": "wKuu6ncyTL7", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary:\nThis paper focuses on the problem of semi-supervised semantic segmentation, where less pixel-level annotations are used to train the network. A new one-stage training framework is proposed to include the process of localization cue generation, pseudo label refinement and training of semantic segmentation. Inspire by recent success in the semi-supervised learning (SSL), a novel calibrated fusion strategy is proposed to incorporate the concept of consistency training with data augmentation into the framework. Experiments on PASCAL VOC and MSCOCO benchmarks validate the effectiveness of the proposed method.  \n\nPro:\n+ The proposed one-stage training framework is elegant compared with two stage methods in this area which include one step for pseudo-label generation and another step for refinement then semantic segmentation training. \n+ The new designed calibrated fusion strategy well incorporate the concept of consistency training with data augmentation into the same framework.\n+ Achieve a new state-of-the-art on both PASCAL VOC and MSCOCO benchmarks compared with recent semi-supervised semantic segmentation methods.\n\nQuestions:\n- CCT (Ouali et al., 2020) includes the consistency training with perturbations which can be treated as a kind of data augmentation on features. I'm wondering if authors can provide some insights about why the proposed method can achieve better performance than CCT when they both include the consistency training and data augmentation in the designs.  \n- In table 3, I suggest to include the segmentation framework used by each method in the table. In early works, old version of deeplab is usually treated as the standard. I understand using deeplab v3 is a fair comparison with CCT. It would be good to make this information clear in the table.\n- It is also suggested to report the performance on PASCAL VOC test set as it is a common practice in this area (although CCT does not do so).\n- Sine the unlabeled data training branch does not rely on any pixel-level annotations, I'm wondering if the proposed method can also work under weakly-supervised setting, where no pixel-level annotations are available during the training. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "s1uKt05JkJh", "reviewer": "AnonReviewer5", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "**Summary:**\n\nThis paper introduces a model to improve semantic segmentation by using a limited amount of pixel-labeled data and unlabeled data or image-level labeled data. The authors use a Self-attention Grad-CAM (SGC) and segmenter to generate the pseudo-labels during training. The approach shows good results on Pascal VOC and COCO datasets and is well analyzed. \n\n\n**Reasons for score:** \n\nI do not think the technical contribution is strong enough for ICLR. The paper is incremental and the proposed approach is a combination of a lot of existing approaches. But I also want to highlight that the experimental section is strong and detailed.\n\n\n**Pros:**\n\n- The idea of using pseudo-labels is interesting because it allows to build larger dataset without increasing the annotation cost.\n- The approach is evaluated in the settings of using unlabeled data and using image-level labeled data.\n- The ablation study section gives a lot of details about the model. The authors analyzed a lot of things: expected calibration error, hypercolumn feature, soft vs hard label, temperature sharpening, color jittering strength, backbone architecture.\n- The approach shows good results on Pascal VOC and COCO datasets.\n- The proposed method achieves good performance in the low-data regime\n\n\n**Cons:**\n- The overall approach seems incremental because it is a combination of a lot of existing approaches and there is not a strong technical contribution. For instance, the model uses several loss functions and all the losses are jointly optimized.\n- I think the related work section should be in the main paper instead of the supplementary. \n- I feel some parts are a bit difficult to read because of some misleading information. For example, the title of section 3.1 is “Experiments using unlabeled data” but the model still uses some labeled data. \n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors introduce an approach for designing pseudo-labels in semi-supervised segmentation.
The approach combines the idea a refining pseudo-labels with self-attention grad-CAM (SGC) and a calibrated prediction fusion, and consistency training by enforcing pseudo labels to be robust to strongly-augmented data. 

The reviewers overall like idea and point out the good level of performance obtained by the method in the challenging semi-supervised context. However, they also point out the limited novelty of the approach, and the need for a better positioning with respect to related works. After rebuttal, reviewers were satisfied with authors' answers and paper modifications, and all recommend acceptance. \
The AC considers that the submission is a nice combination of existing techniques and likes the simplicity of the one-stage approach, which reaches good performances. Therefore, the AC recommends acceptance.
Decision: Accept (Poster)