Weak-shot Semantic Segmentation via Dual Similarity Transfer
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Abstract
Semantic segmentation is a practical and active task, but severely suffers fromthe expensive cost of pixel-level labels when extending to more classes in widerapplications.
To this end, we focus on the problem named weak-shot semanticsegmentation, where the novel classes are learnt from cheaper image-level labelswith the support of base classes having off-the-shelf pixel-level labels.
To tacklethis problem, we propose a dual similarity transfer framework, which is builtupon MaskFormer to disentangle the semantic segmentation task into single-labelclassification and binary segmentation for each proposal.
Specifically, the binarysegmentation sub-task allows proposal-pixel similarity transfer from base classesto novel classes, which enables the mask learning of novel classes.
We also learnpixel-pixel similarity from base classes and distill such class-agnostic semanticsimilarity to the semantic masks of novel classes, which regularizes the segmentation model with pixel-level semantic relationship across images.
In addition, wepropose a complementary loss to facilitate the learning of novel classes.
Comprehensive experiments on the challenging COCO-Stuff-10K and ADE20K datasetsdemonstrate the effectiveness of our method.
Introduction
Semantic segmentation [23, 5, 37, 39] is a fundamental and active task in computer vision, whichaims to produce class label for each pixel in image.
Training modern semantic segmentation modelsusually requires pixel-level labels for each class in each image, and thus costly datasets have beenconstructed for learning.
As a practical task, there is a growing requirement to segment more classesin wider applications.
However, annotating dense pixel-level mask is too expensive to cover thecontinuously increasing classes, which dramatically limits the applications of semantic segmentation.
In practice, we have some already annotated semantic segmentation datasets, e.g. , COCO-80 [21].
These costly datasets focus on certain classes ( e.g. , the 80 classes in [21]), and ignore other uninterested classes ( e.g. , other classes beyond the 80 classes in [21]).
Over time, there may be demandto segment more classes, e.g. , extending to COCO-171 [2].
We refer to the off-the-shelf classeshaving already annotated masks as base classes, and those newly covered classes as novel classes.
Asillustrated in Fig.
1 (a), one sample in COCO-80 focuses on segmenting cat , bed , etc, but ignoresthe lamp .
Existing scenario for expansion in [2] is to annotate the dense pixel-level masks for novelclasses again ( i.e. , annotating the mask for lamp in Fig.
1 (a)), which is too expensive to scale-up.
Tothis end, we focus on a cheaper yet effective scenario in this paper, where we only need to annotateimage-level labels for the novel classes.
We refer to our learning scenario as weak-shot semantic segmentation, which focuses on furthersegmenting novel classes by virtue of cheaper image-level labels with the support of base classeshaving pixel-level masks.
Specifically, given a standard semantic segmentation dataset annotated only for base classes (the hide in that would like to all the
and classes in the stage.
Similar been explored in
43], but they 1)further assume that the off-the-shelf dataset contains no novel classes; 2) assume that the backgroundclass (in PASCAL VOC dataset [9]) consisting of all ignore classes is annotated with pixel-levelmask.
However, these two assumptions are hard to satisfy in real-world applications, because theignore region of off-the-shelf dataset may contain novel classes and we actually cannot have themasks of background class before having the masks of novel classes.
In contrast, our scenario issimilar in spirit but more succinct and practical, which is well demonstrated by the aforementionedinstance expanding from COCO-80 to COCO-171.
Besides, RETAB [43] only conducted experimentson PASCAL VOC, while we focus on more challenging datasets ( i.e. , COCO-Stuff-10K [2] and
ADE20K [42]), without a background class annotated with masks.
In weak-shot semantic segmentation, the key problem is how to learn dense pixel-level masks fromthe image-level labels of novel classes with the support of pixel-level masks of base classes.
Ourproposed solution is dual similarity transfer, which is built upon MaskFormer [8] as shown in Fig. 1(b).
We choose MaskFormer [8] because it disentangles the segmentation task into two sub-tasks:proposal classification and proposal segmentation.
Specifically, MaskFormer [8] produces someproposal embeddings ( aka per-segment embeddings in [8]) from shared query embeddings for inputimage, each of which is assigned to be responsible for one class present in image (allowing emptyassignment).
Then MaskFormer performs proposal classification sub-task and proposal segmentationsub-task for each proposal embedding according to their assigned classes respectively.
In our setting and framework, the proposal embeddings of base classes are supervised in both subtasks using class and mask annotations, while the proposal embeddings of novel classes are supervisedonly in classification sub-task due to lacking mask annotations.
The proposal segmentation subtask is essentially learning proposal-pixel similarity.
Such similarity belongs to pair-wise semanticsimilarity, which is class-agnostic and transferable across different categories [6, 29, 4].
Therefore,proposal segmentation for novel classes could be accomplished based on the proposal-pixel similaritytransferred from base classes.
To further improve the quality of binary masks of novel classes,we additionally propose pixel-pixel similarity transfer, which learns the pixel pair-wise semanticsimilarity from base classes and distills such class-agnostic similarity into the produced masks ofnovel classes.
In this way, the model is supervised to produce segmentation results containingsemantic relationship for novel classes.
Inspired by [31, 34, 30], we learn from and distill tocross-image pixel pairs, which could also introduce global context into model, i.e. , enhancing thepixel semantic consistency across images.
In addition, we propose a complementary loss, based onthe insight that the union set of pixels belonging to base classes is complementary to the union set ofpixels belonging to novel classes or ignore class in each image, which provides supervision for theunion of masks of novel classes.
We conduct extensive experiments on two challenging datasets ( i.e. , COCO-Stuff-10K [2] and
ADE20K [42]) to demonstrate the effectiveness of our method.
We summarize our contributions as
1) We propose a dual similarity transfer framework for weak-shot semantic segmentation, in whichMaskFormer lays the foundation for proposal-pixel similarity transfer.2)
We propose pixel-pixel similarity transfer, which learns pixel-pixel semantic similarity from baseclasses and distills such class-agnostic similarity to the segmentation results of novel classes.
We also
propose a complementary loss to facilitate the mask learning of novel classes.3) Extensive experiments on the challenging COCO-Stuff-10K [2] and ADE20K [42] datasetsdemonstrate the practicality of our scenario and the effectiveness of our method.
Related Works
Weakly-supervised Semantic Segmentation (WSSS).
Considering the expensive cost for annotatingpixel-level masks, WSSS [26, 10, 28, 16] rely on only image-level labels to train the segmentationmodel, which has attracted increasing attention.
The major WSSS methods [3, 1, 33] firstly train aclassifier to obtain class activation map (CAM) [41] to derive pseudo masks, which are then used totrain a standard segmentation model.
For example, SEC [14] proposed “seed, expand, and constrain”three principles, which produced a profound impact to WSSS.
Under similar pipeline, some works[18, 3, 33] focus on enhancing the seed, while some other works [13, 32, 38] pay attention toimproving the expand strategy.
Although WSSS has achieved great success, the expanded CAM isdifficult to cover the full semantic class due to lacking annotation about the spatial scope of class.
Fortunately, in our focused problem, such information could be derived from an off-the-shelf datasetand transferred to facilitate the learning of classes with only image-level labels.
Weak-shot Learning.
Recently, weak-shot learning focusing on learning novel classes from cheapweak labels with the support of a set of base classes having off-the-shelf strong labels has beenexplored in image classification [4], object detection [22, 40, 7, 19], and instance segmentation [15],which has achieved promising success.
In weak-shot semantic segmentation [43], the problem islearning to segment novel classes with only image-level labels with the support of base classes havingpixel-level labels.
Concerning the task setting, as aforementioned, RETAB [43] further assumes thatthe off-the-shelf dataset has no novel classes and the background class is annotated with pixel-levelmask, while the setting in this paper is more succinct and practical.
Concerning the technical method,
RETAB [43] follows the framework of WSSS, which suffers from a complex and inconvenient multistage pipeline, i.e. , training classifier, deriving CAM, expanding to pseudo-labels, and re-training.
In contrast, we build our framework based on MaskFormer [8] to perform dual similarity transfer,which could achieve satisfactory performance in single-stage without re-training.
Similarity Transfer.
As an effective method, similarity transfer has been widely applied in varioustransfer learning tasks [6, 29, 4].
Specifically, semantic similarity (whether the two inputs belong tothe same class) is class-agnostic, and thus is transferrable across classes.
To name a few, Guo et al .[12] transferred class-class similarity and sample-sample similarity across domains in active learning.
PrototypeNet [27] proposed to learn and transfer the similarity between image and prototype acrosscategories for both few-shot classification and zero-shot classification.
In this paper, we propose totransfer proposal-pixel similarity and pixel-pixel similarity for weak-shot semantic segmentation.
Thetwo kinds of similarities both belong to semantic similarity, which are transferrable across classes.
Methodology
In our weak-shot semantic segmentation, we are given a standard segmentation dataset annotatedfor base classes C b , and we would like to further segment another set of novel classes C n ignored inthe off-the-shelf dataset, where C b ∩ C n = ∅ .
We assume that we have the image-level labels for C n ,which is rather cheaper and more convenient to obtain than pixel-level mask.
3.1 Review of MaskFormer
In this section, we briefly review MaskFormer [8], which lays the foundation of our framework.
The general pipeline of MaskFormer disentangles semantic segmentation task into two sub-tasks:proposal classification and proposal segmentation.
Specifically, MaskFormer maintains N learnablequery embeddings Q ∈ R C × N shared for all images, as shown in the upper part of Fig.
Whenthe image is input, the backbone features are extracted via a backbone.
Then, N query embeddings attend to backbone features to produce proposal embeddings E prop ∈ R C × N via a transformerdecoder.
For each proposal embedding, a bipartite matching algorithm assigns a class present inthe input image to it, considering the classification loss and mask loss as the cost for assignment.
For proposal classification sub-task, each proposal embedding is fed to a linear classifier to yieldclass probability predictions
Y ∈ R ( K +1)
× N over K semantic classes and 1 ignore class.
Forproposal segmentation sub-task, pixel embeddings E pix ∈ R C
× H × W are extracted from backbonefeatures via a pixel decoder.
Afterwards, proposal embeddings are processed by several FC layersand computes dot-product followed by sigmoid with pixel embeddings to produce binary masks
M ∈ R N × H × W , i.e. , M [ i, h, w ] = sigmoid( E prop [: , i ] · E pix [: , h, w ]) .
In the training stage, thetwo sub-tasks for each proposal embedding are supervised by the label and mask of assigned class(the mask loss of ignore class is eliminated).
In the test stage, the semantic segmentation result foreach class at pixel ( h, w ) is obtained by summarizing all the masks weighted by the class score, i.e. ,arg max c ∈{ 1 ,...,K } (cid:80) Ni =1 Y [ c, i ] · M [ i, h, w ] .
For more details, please refer to MaskFormer [8].
3.2 Proposal-Pixel Similarity Transfer on MaskFormer
In our setting, we have only image-level labels for novel classes and we choose to produce pixel-levelmasks via proposal-pixel similarity transfer based on MaskFormer [8].
For concise description, werefer to the proposal embeddings assigned with base ( resp. , novel or ignore) classes as base ( resp. ,novel or ignore) proposal embeddings.
We refer to the binary ( resp. , novel or ignore) masks producedfrom base proposal embeddings as base ( resp. , novel or ignore) masks.
We refer to the image pixelsactually belonging to base ( resp. , novel or ignore) classes as base ( resp. , novel or ignore) pixels.
Our method makes full use of the disentangled two sub-tasks of MaskFormer to perform proposalpixel similarity transfer.
The proposal classification sub-task assigns class labels to proposal embeddings.
The proposal segmentation sub-task calculates the similarity between each proposal embeddingand all pixels to obtain a similarity map, which is the segmentation mask corresponding to the classof this proposal embedding.
Such proposal-pixel similarity is class-agnostic and thus transferableacross classes.
In this way, our method could produce novel masks via the similarity transferred frombase classes.
Our framework is illustrated in Fig.
2, where the upper part is the same as MaskFormer producingproposal embeddings E prop ∈ R C × N .
Because we have no mask for novel classes in the bipartitematching algorithm, we omit the mask loss in the cost if the class to be assigned belongs to C n .
For theproposal classification sub-task, all proposal embeddings are supervised to predict the assigned classesbecause we have all the image-level labels of each input image.
Thus, we have the classification loss
[Equation]
where y ∗ ( i ) indicates the assigned single ground-truth (GT) class of the i -th proposal embedding.
For the binary segmentation sub-task, only the base masks are supervised as
[Equation]
where M ∗ ( y ∗ ( i )) indicates the GT mask of the assigned class of the i -th proposal, and L focal+dice isa binary mask loss consisting of a focal loss [20] and a dice loss [24] as in MaskFormer [8].
Underthe supervision of mask loss, the base proposal embeddings will produce a binary similarity with allthe pixel embeddings, i.e. , each proposal embedding is similar or dissimilar to each pixel embedding.
Such semantic pair-wise similarity is class-agnostic, and thus could transfer across classes.
Therefore,although the novel proposal embeddings are not supervised by GT masks, we can calculate thesimilarity between novel proposal embeddings and pixel embeddings to produce novel masks.
3.3 Pixel-Pixel Similarity Transfer via Pixel SimNet
Due to lacking semantic supervision of novel classes, the novel masks are hard to precisely reacha semantic consistency across training images, so we design a novel strategy to learn the pair-wisesemantic similarity from base pixels across images and distill such class-agnostic similarity to novelpixels across images, which could complement to the above proposal-pixel similarity.
Specifically, to learn pixel-pixel similarity across images, for each input image, we randomly sampleanother training image as its reference image.
According to image-level labels, we ensure thatthe input image and reference image have common base and novel classes.
We train a pixel-pixelsimilarity net (SimNet) using base pixels which are associated with GT pixel-level labels.
By classaware random sampling J base pixels from both images, we construct J × J ( J = 100 in ourexperiments) pixel pairs balanced in binary semantic class ( i.e. , similar v.s. dissimilar ).
We denotethe sampled pixel embeddings from input image ( resp. , reference image) as E pix − inp ∈ R C × J ( resp. ,
E pix − ref ∈ R C × J ).
A SimNet ( 3 FC layers followed by sigmoid function) is employed to predict thesimilar score, which can be formulated as
[Equation]
where ⊕ indicates the feature concatenation of every two pixels between the two pixel sets and
R b ∈ R J × J is the similar score between every pixel pair.
Classification loss is used for training:
[Equation]
where R ∗ b is the GT similarity label derived from pixel-level annotations from base region.
To distill the learned pixel-pixel semantic similarity into the segmentation of novel pixels, we alsoconstruct J × J pixel pairs from the not-base region (ignore pixels are rather fewer relatively to novelpixels) of both images respectively and obtain E pair − n ∈ R ( C + C ) × J × J .
Because the input imageand reference image have common novel classes, the sampled pixel pairs have chance to belongto the same novel classes, which could inject semantic consistency ( i.e. , similar class) into novelpixels.
We use the SimNet to estimate the GT semantic similarity and obtain R n ∈ R J × J , which isdetached of gradient and employed as the distillation source.
For the distillation target, we computethe segmentation scores of novel classes at sampled pixel, i.e. , S n = { (cid:80) Ni =1 Y [ c, i ] · M [ i, : , :] } c ∈C n ,and collect the scores of pixel sampled from input image S input − n ∈ R |C n |× J and the scores from reference image S ref − n ∈ R |C n |× J .
The pixel-pixel similarity distillation from base classes to novelclasses is achieved by
[Equation]
where Cos enum ( · , · ) computes the cosine similarity of all enumerated pixel pairs between the twosets of |C n | -dim vectors, Relu( · ) focuses on the non-negative part considering that zero value meansindependent and thus dissimilar, and BCE( · , · ) is the binary cross entropy function for pushing thedistillation target towards the distillation source.
In this way, the pixel-pixel similarity is learned frombase classes and transferred to facilitate the semantic consistency of novel masks across images.
3.4 Complementary Loss
Although we have no pixel-level annotation for novel classes, we have the prior knowledge that theunion set of novel pixels and ignore pixels and the union set of base pixels are complementary ineach image.
Therefore, we use a complementary loss to inject such insight into our model:
[Equation]
where the first input is the complementary set of the union set of all the GT masks of base classes, thesecond input is the union set of all the binary masks produced by the proposal embeddings assignedwith novel classes or ignore class, and the union operator specifically takes the maximum valueover involved binary masks at each pixel.
Because the ignore mask produced from ignore proposalembedding is meaningless here as in MaskFormer [8], we reset a constant value γ (default 0 . 1 ) forthe ignore mask, which is a prior probability for the ignore class.
The constant prior probability issimilar to the background threshold used in weakly supervised semantic segmentation [17, 33, 35].
3.5 Training and Inference
Our training and inference pipelines are the same as MaskFormer [8], and the full training loss is
[Equation]
where L cls and L mask implicitly include the default trade-off hyper-parameters in MaskFormer, and
L sim is actually also a classification loss and balances well with L cls .
We use α (default value 0 . 1 )for balancing the distillation loss and β (default value 0 . 2 ) for balancing the complementary loss.
Experiments
4.1 Datasets, Evaluation, and Implementation Details
We explore the learning scenario and evaluate our method on two challenging datasets.
COCO-Stuff10K [2] contains 9k training images and 1k test images, covering 171 semantic classes.
ADE20K
[42]has 20k training images and 2k validating images, covering 150 semantic classes.
For the learningscenario, we random split all the classes in the dataset into base classes and novel classes.
Then wekeep the image-level labels of novel classes in each training image, and reset the pixel-level masksof novel classes to ignore class ( e.g. , class ID 255 ).
We follow the split ratio |C b | : |C n | = 3 : 1 in
RETAB [43], which are also widely used in few-/zero-shot learning [25, 11].
To show the performancemore clearly, we focus on mean IoU of novel classes, because the base classes are always supervisedby GT masks and show relatively robust performances.
More details are left to Supplementary.
4.2 Comparison with Prior Works
Comparative Baselines.
We set three groups of baselines: 1) representative WSSS methods, including SEAM [33], CONTA [35], CPN [36], and RIB [17]; 2) weak-shot segmentation segmentationmethods, including RETAB [43]; 3) the standard segmentation method, a MaskFormer [8] trainedwith GT masks of novel classes, named FullyOracle .
Because WSSS baselines and RETAB all needa re-training stage to achieve the final performance and there are GT masks of base classes in trainingimages, we re-train all baselines with mixed labels.
Specifically, the mixed labels consist of GTmasks of base classes and pseudo-labels of novel classes generated by the model ( e.g. , deriving and
[Table]
expanding CAM).
For a fair comparison, we re-train each baseline on the standard MaskFormer [8]using the generated mixed labels.
Therefore, all the methods are consistent in network architecture.
Our method could also be appended with a re-training stage for further improvement.
We use themixed labels consisting of GT masks of base classes and pseudo-labels of novel classes predicted byour model (instead of using CAM) in the re-training stage.
More details are left to Supplementary.
Quantitative Comparison.
We summary all the results on four random splits of two datasets in
Tab. 1.
Even without re-training, our method can dramatically outperforms WSSS baselines and
RETAB [43] ( e.g. , about 2 .
6% against RETAB)
, demonstrating the effectiveness of our method basedon dual similarity transfer.
By appending a re-training stage as in WSSS, our method is furtherimproved by about 2 . 2% .
Compared with the fully-supervised upper bound Fully Oracle , our methodwith re-training could achieve satisfactory performances ( i.e. , about 78% ∼ 92% of the upper bound)on the two challenging datasets, showing the potential of our dual similarity transfer framework forlearning novel classes from only image-level labels.
Qualitative Comparison.
We conduct qualitative comparison for the re-trained WSSS baseline
RIB [17], RETAB [43] and our method on both datasets.
As shown in Fig.
3, WSSS baseline tendsto produce incomplete masks, and our method could produce more complete and precise semanticmasks against RETAB [43].
Even in complex scenes ( e.g. , the 2nd and 3rd rows), our method stillpredicts preferable results for novel classes.
More visualizations are left to Supplementary.
[Table] [Table]
4.3 Module Analysis
Proposal-pixel Similarity Transfer.
As shown in the 1st row of Tab. 2, only with proposal-pixelsimilarity transfer, the model could achieve acceptable performance on various dataset splits, whichlays the foundation of our method.
We provide in-depth qualitative visualization in Fig.
4, fromwhich we could directly inspect the single-label classification and binary segmentation sub-tasks ofeach proposal embedding.
Overall, the predicted classes are precise and confident, and the producedmasks of proposal embeddings completely cover the corresponding semantic classes.
Although Truckis actually not in the first example, the class score and binary mask are both relatively lower, and thusthe fused result will not severely degrade the final segmentation performance.
Pixel-pixel Similarity Transfer.
As shown in the 2nd row of Tab. 2, we solely enable pixelpixel similarity transfer on the foundation, which leads to dramatical improvement.
The in-depthquantitative analysis about the transferability is shown in Tab.
We collect base pixel pairs and novelpixel pairs sampled from 100 training image pairs, and employ GT labels to evaluate the similaritiespredicted by our SimNet.
From the table, we can find that the semantic similarity learned from baseclasses is highly transferrable across classes.
The in-depth qualitative visualization is given in Fig.
5,where we can see that for each sampled pixel, SimNet could “activate” the pixels belonging to thesame semantic class across images.
Therefore, the transferred pixel-pixel similarity could provideeffective semantic relationship for distilling to regularize novel classes.
The Fig.
6 (a) shows theimpact of α (controlling the strength of distillation) to our model.
Specifically, we change α in a rangewhile fixing the other hyper-parameters, and depict the results obtained by our full-fledged model.
Thus, our method is relatively robust to α in an appropriate range.
Other experimental analysesincluding reference image selection and point number selection are left to Supplementary.
Complementary Loss.
As shown in the 3rd row of Tab. 2, we solely enable our complementaryloss on the foundation, which also results in obvious promotion.
In the 4th row of Tab. 2, with allthe modules enabled, our full-fledged model achieves the optimal performance.
The Fig.
6 (b,c)illustrate the impact of β (controlling the strength of injecting complementary insight) and γ (theprior probability for the ignore class) to our model.
Larger γ will block the supervision to novelmasks and degrade the performance.
Smaller γ will “over-activate” novel classes on ignore pixels,which has relatively less impact.
Overall, our model is robust to β and γ in an appropriate range.
4.4 Generalization Ability for Dataset Expansion
Focusing on a problem that uses cheaper labels to expand the off-the-shelf dataset, we naturally wonder the generalization ability of our method in such expansion.
We investigate by further manipulatingthe datasets ( e.g. , considering more novel classes).
The experiments are left to Supplementary.
4.5 Limitation Discussion and Significance Test
We discuss the limitation and provide failure cases to further analyse our method.
We also show thatour method is statistically significant by significance test.
The two sections are left to Supplementary.
Conclusion
In this paper, we focus on the problem of weak-shot semantic segmentation, which makes full use ofthe off-the-shelf pixel-level labels of base classes to support segmenting novel classes with imagelevel labels.
For such problem, we have proposed a dual similarity transfer framework, which is builtupon MaskFormer for performing proposal-pixel similarity transfer.
To further regularize the modelwith pixel-level semantic relationship, we learn pixel-pixel similarity from base classes and distill suchclass-invariant similarity to novel masks.
We also have proposed a complementary loss for facilitatingthe mask learning of novel classes.
Extensive experiments on the challenging COCO-Stuff-10K and
ADE20K datasets have demonstrated the effectiveness of our proposed method.
Checklist
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