Implementation details Training protocol sec:training-protocol In this
section, we provide additional details on the training protocol used in our
experiments. Note that we adopt the same protocol for all the methods, to
ensure a fair comparison.

When fine-tuning we follow chen2018encoder, using SGD as optimizer with
momentum $0.9$, weight decay $10^{-4}$ and a polynomial learning rate policy,
$\text{lr}=\text{lr}_{\text{init}}(1-\frac{\text{iter}}{\max\\_\text{iter}})^{0.9}$.
During training, we apply the same data augmentation of chen2018encoder,
performing random scaling and horizontal flipping, with a crop-size of
$512\times 512$. While the previous hyperparameters are shared across
settings, we use a different learning rate and number of training iterations
depending on the dataset, number of shots and learning steps. In particular,
in the base step we train the network for 30 epochs on Pascal-VOC and 20
epochs on COCO using learning rate $10^{-2}$ and batch size 24. For the FSL
step t, we set the batch size to $\min(10,|\set D_{t}|)$. In the FSL steps of
VOC-SS, we train for 1000 iterations with learning rate $10^{-3}$, and for 200
iterations per step for VOC-MS, with learning rate $10^{-4}$. On COCO FSL
steps we use a learning rate $10^{-3}$, training the model for 2000 iterations
on COCO-SS, and 100 iterations on every step of COCO-MS. These training
hyperparameters are shared by all methods.

Adapting baselines to iFSS and hyperparameters choice In this section, we
describe how we adapt the baselines reported in the main paper to iFSS and the
value of their hyperparameters. We set the hyperparameters specific of each
approach in the VOC-SS and COCO-SS 1-shot settings, using the first split of
each dataset (5-0 for VOC, and 20-0 for COCO) and maintaining the same values
across all other shots, splits and number of learning steps.

For Weight Imprinting (WI), we adapted the work of qi2018low from image
classification. In particular, we replaced the image-level feature extractor
of qi2018low with masked average pooling (MAP), as described in Sec. 4.1 of
the paper. This method does not require additional hyperparameters, and we
initialize the prototypes for new classes while keeping the prototypes of old
ones unaltered.

Similarly, for Dynamic Weight Imprinting gidaris2018dynamic (DWI), we
implemented the classifier using the same attention mechanism and weight
generator of gidaris2018dynamic, but we replaced the class-specific image-
level features with the ones extracted through MAP. DWI uses a second meta-
learning training stage on the base classes to refine the weight generator. We
performed this step for $1000$ iterations, with learning rate $1.0$ and batch
size 8, aggregating the gradient of 2 training episodes, as in
gidaris2018dynamic. After the meta learning stage, the method includes new
classes in the FSL steps by weight imprinting, that we implemented with MAP,
as for WI.

Rethinking FSL tian2020rethinking (RT), refines the model during the base step
by using self-distillation and fine-tunes the classifier on the FSL step. In
particular, after the base step a copy of the model is stored and provides the
target of the self-distillation loss, to the current model. This training
phase uses the same hyperparameters of the base one. We stopped after one
additional training round because we did not see clear improvements using more
rounds at the expense of a longer training time. For iFSS, we applied the
self-distillation loss pixel-wise. For the FSL steps, we trained the
classifier for new classes starting from random weights and freezing the rest
of the network. We multiplied the learning rate by 10 the Sec sec:training-
protocol on the FSL step, since this improved the performance on both
datasets.

Adaptive Masked Proxies siam2019adaptive (AMP) has been implemented following
details in siam2019adaptive uses a standard linear classification layer and,
an L2-normalized MAP features as classifier for the new classes. We adapted
AMP to work on both old and new classes using all the available annotations in
the FSL dataset. In particular, as proposed by siam2019adaptive for continuous
segmentation, we update the classifier weights for all the old classes
appearing in the new dataset by computing a moving average with update rate
$\alpha=0.25$.

For Semantic Projection Network xian2019spnet (SPN) we follow the
implementation provided by the authors, using the combination of word2vec
mikolov2013distributed and fastText joulin2016fasttext as class embeddings,
using them direclty as classifier weights. The method has no specific
hyperparameters and we adapt it to by not retaining the old datasets in the
learning steps.

We implemented the three incremental learning methods, Learning without
Forgetting li2017learning (LwF), Incremental Learning Techniques
michieli2019incremental (ILT), and Modeling the Background
cermelli2020modeling (MiB), following the code provided by
cermelli2020modeling. As regularizer, LwF and ILT apply a standard cross-
entropy loss using the old network predictions as target, and ILT imposes an
additional L2 constraint on the output of the backbone (the Resnet-101). MiB
uses the revised cross-entropy and distillation losses as well as the
classifier weights initialization for new classes. The weight of the
distillation losses is $100$ for LwF, $100$ on both the L2 and the cross-
entropy for ILT, and $10$ for MiB.

Finally, for our model we set $\lambda$ to $10$ for all settings. We recall
that for all the baseline we report in the paper, we used the same training
protocol and architectures of .

split |  classes
---|---
5-0 |  aeroplane, bicycle, bird, boat, bottle
5-1 |  bus, car, cat, chair, cow
5-2 |  table, dog, horse, motorbike, person
5-3 |  plant, sheep, sofa, train, tv-monitor
Table : Pascal-VOC class split. split |  classes
---|---
20-0 |  person, airplane, boat, parking meter, dog, elephant, backpack, suitcase, sports ball, skateboard, wine glass, spoon, sandwich, hot dog, chair, dining table, mouse, microwave, refrigerator, scissors
20-1 |  bicycle, bus, traffic light, bench, horse, bear, umbrella, frisbee, kite, surfboard, cup, bowl, orange, pizza, couch, toilet, remote, oven, book, teddy bear
20-2 |  car, train, fire hydrant, bird, sheep, zebra, handbag, skis, baseball bat, tennis racket, fork, banana, broccoli, donut, potted plant, tv, keyboard, toaster, clock, hair drier
20-3 |  motorcycle, truck, stop sign, cat, cow, giraffe, tie, snowboard, baseball glove, bottle, knife, apple, carrot, cake, bed, laptop, cell phone, sink, vase, toothbrush
Table : COCO class split.

###  Dataset class splits

We split both Pascal-VOC and COCO in 4 folds, following previous works in
semantic segmentation [nguyen2019feature, wang2019panet, zhang2019canet,
shaban2017one, rakelly2018few]. Table  reports the detailed class folds for
Pascal-VOC, taken from [shaban2017one], and Table  the ones for the COCO
dataset, taken from [nguyen2019feature].

0.45 [width=]fig/rebuttal/VOC_perc.png 0.45 [width=]fig/rebuttal/COCO_perc.png

Figure : VOC Figure : COCO Figure : Percentage of images containing the old
class in the 5-shot setting datasets. We note that class person has class-ID
15 on VOC and 1 on COCO. The red line represents the median over all classes.

##  1 Influence of old classes annotations in iFSS

The few-shot learning steps (FSL) in our benchmark consider a dataset with 1,
2, or 5 images for each new class, randomly sampled from the set of images
containing at least one pixel of that class, but without imposing any
constraint about the presence of old classes. However, differently from
[cermelli2020modeling], the few-shot datasets provide annotation for all
available pixels, both for new and old classes. In this section, we first
analyze the frequency with which old classes appears in few-shot learning
steps, showing that they co-occurs rarely with new classes. Then, we compare
the results of the settings using (non-strict IL) and not using (strict IL)
old classes annotations.

###  Frequency of old classes in few-shot learning datasets

Fig. reports the percentage of images per old class averaged on all folds of
the 5-shot settings. From the figure, we note that old classes rarely co-occur
when learning new classes: the median is 1.56% of images per old class on VOC
and 2.1% on COCO. The only exception is the person class that frequently
appears in the few-shot dataset: in 32% of images in VOC and in 52% in COCO.
Moreover, we note that many classes never appear with new classes, both in
VOC, such as classes 4 (boat) and 10 (cow), and in COCO, classes 22 (bear) and
23 (zebra).

!  VOC-SS COCO-SS 1-shot 2-shot 5-shot 1-shot 2-shot 5-shot Method Strict
mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM rowColor FT 58.2 9.7 16.6 59.1 19.6 29.5 55.8 29.5
38.6 41.2 4.1 7.5 41.5 7.3 12.4 41.6 12.3 19.0 FT 55.0 10.2 17.2 55.5 19.2
28.5 43.7 26.8 33.2 35.3 4.5 8.0 32.8 7.4 12.1 26.9 11.1 15.7 rowColor WI
[qi2018low] 62.6 15.4 24.8 63.2 19.2 29.4 63.2 21.7 32.3 43.8 6.9 11.9 44.2
7.9 13.5 43.6 8.7 14.6 WI [qi2018low] 62.6 15.4 24.8 63.2 19.2 29.4 63.2 21.7
32.3 43.8 6.9 11.9 44.2 7.9 13.5 43.6 8.7 14.6 rowColor SPN [xian2019spnet]
59.8 16.3 25.6 60.7 26.3 36.7 58.3 33.4 42.4 43.5 6.7 11.7 43.7 10.2 16.5 43.7
15.6 22.9 SPN [xian2019spnet] 56.3 16.4 25.4 57.0 25.3 35.1 48.6 30.2 37.3
38.1 7.0 11.8 37.0 10.4 16.3 33.2 15.1 20.8 rowColor MIB
[cermelli2020modeling] 61.0 5.2 9.6 63.5 12.6 21.1 64.9 28.1 39.2 43.8 3.5 6.5
44.4 6.0 10.6 44.7 11.9 18.8 MIB [cermelli2020modeling] 61.0 6.0 11.0 63.5
13.7 22.5 64.9 29.4 40.4 43.7 4.2 7.7 44.2 7.1 12.3 44.4 13.8 21.1 rowColor
PIFS 60.8 18.5 28.4 60.5 26.3 36.7 60.0 33.2 42.8 40.8 8.2 13.6 40.9 11.1 17.5
42.8 15.7 23.0 PIFS 59.1 18.2 27.9 58.8 26.1 36.2 57.2 32.5 41.5 34.9 8.9 14.2
34.6 11.7 17.4 32.6 15.6 21.1 PIFS* 60.3 18.0 27.7 60.3 26.3 36.6 59.5 33.0
42.5 38.8 8.8 14.4 39.2 11.8 18.1 38.4 16.1 22.6

Table : Performance in strict and non-strict incremental learning on single-
step settings. In bold-red the best method in strict-IL scenario. In bold-
blue, the best method in non strict-IL. PIFS* uses the revised classification
loss proposed by MIB [cermelli2020modeling].

###  Comparison between iFSS in strict and non-strict settings

Tab. 1 reports the comparison among the strict and non-strict setting of some
indicative methods, FT, WI [qi2018low], SPN [xian2019spnet], MIB
[cermelli2020modeling] and PIFS, evaluating the impact of the background shift
on them. We also report PIFS*, which uses the revised classification loss
proposed by MIB [cermelli2020modeling] to deal with the background shift.
First, we note that WI obtains the same results in the two settings since it
is not affected by the annotation on old classes and it only uses new classes’
pixels for generating the classifier weights. Differently, FT and SPN, suffer
the background shift, as indicated by the large decrease in mIoU-B on all
setting. MIB, being designed to solve deal with the background shift, even
improves its performance, obtaining similar results in mIoU-B and improving
its performance on mIoU-N. Finally, we note that PIFS is robust to the
background shift on VOC but it decrease in performance on mIoU-B on COCO.
Moreover, it obtains outstanding performance on new classes, constantly
outperforming the competitors on mIoU-N. Introducing the cross-entropy loss of
MiB [cermelli2020modeling] in PIFS, it notably improves the results on old
classes, alleviating the background shift. We remark that introducing the loss
of [cermelli2020modeling] is straightforward, since the choice of
classification loss is independent from the prototype-learning and the
distillation loss of PIFS.

0.3 [width=]fig/plot22/voc1.pdf 0.3 [width=]fig/plot22/voc2.pdf 0.3
[width=]fig/plot22/voc5.pdf 0.056 [width=]fig/plot22/legend.pdf 0.3
[width=]fig/plot22/coco1.pdf 0.3 [width=]fig/plot22/coco2.pdf 0.3
[width=]fig/plot22/coco5.pdf 0.056 [width=]fig/plot22/legend.pdf

Figure : VOC 1-shot Figure : VOC 2-shot Figure : VOC 5-shot Figure : COCO
1-shot Figure : COCO 2-shot Figure : COCO 5-shot Figure :  results on the
sequential addition of new class. Every column is a new step.

##  2 Additional results

###  Detailed results step-by-step

We report the results for every incremental step on the VOC-MS and COCO-MS
settings in fig:multi-step. For every incremental step, we report the harmonic
mean (HM) between the mIoU on base ($\set C^{0}$) and new ($\set
C^{t}\backslash\set C^{0}$) classes. From the results, we note that fine-
tuning (orange), RT [chen2017rethinking] (green, circle), and incremental
learning methods [cermelli2020modeling, michieli2019incremental,
li2017learning] (blue) obtain the worst performance on all settings. We argue
that this is due to: i) not exploiting prototype learning, failing to
correctly initialize and represent the new classes, and ii) not dealing with
the non-i.i.d. data, as demonstrated by the poor performances obtained on VOC
when few images are provided (1- and 2-shot settings). On the other hand,
methods that perform prototype learning, such as few-shot classification
methods [qi2018low, gidaris2018dynamic] (green) and AMP [siam2019adaptive]
(yellow, square), show a better trade-off between learning and forgetting. In
particular, WI [qi2018low] and DWI [gidaris2018dynamic] achieve good
performance on VOC, being close to (red) especially on the 1-shot setting.
However, we note that on VOC 5-shot and COCO, obtains better performances
since it fine-tunes the network on the few-shot data, obtaining a better
representation while avoiding overfitting. AMP [siam2019adaptive],
differently, is outperformed by , remarking that it is essential to update the
network representation and not only the prototypes during the FSL steps.
Finally, SPN [xian2019spnet] (yellow, triangle) achieves good performance on
the initial steps of COCO-MS 2- and 5-shot settings, even surpassing .
However, after only one (5-shot) or two (2-shot) learning steps its
performances degrade and it is surpassed by . This demonstrates that improves
the representation for new class pixels while better dealing with forgetting
and non-i.i.d. data, even without using external knowledge. Overall, is
consistently the best on every dataset and shot.

###  Detailed results for each split

Due to space constraints, in the main paper we report the average results
across the 4 splits of classes of each dataset. Here, we report the detailed
results in all folds separately, measuring them as the mIoU on base (mIoU-B)
and new (mIoU-N) classes, and their harmonic mean (HM). Pascal VOC. We report
the results for Pascal-VOC with only one few-shot learning step (VOC-SS) on
Tab. 2 for 1-shot, on Tab. 2 for 2-shot, and on Tab. 2 for 5-shot. The results
on each fold are consistent with their average. is effective on all the VOC
folds, being always the best on 1-shot, and always the best or second best on
2-shot scenario, in terms of HM. Moreover, we note that SPN is the second best
on both 2-shot and 5-shot scenarios, on all folds. On 5-shot scenario
incremental learning methods become competitive to , being ILT the best on 5-0
($+1.7$% HM ) and LwF on 5-1 ($+3.4$% HM ). However the improvement of these
methods is not consistent in other folds (5-3, where improves ILT of 5.1% in
HM), obtaining an average performance lower than . Tables 2, 2, and 2 report
the results (averaged per FSL step) for the multi step scenario (VOC-MS), for
the 1, 2, and 5 shot respectively. We remark that this setting is particularly
challenging since methods are provided only with 1, 2 or 5 images of the same
class to train, resulting in very unbalanced and non-i.i.d set. In this
setting, obtains the best results on 5-1, 5-2, and 5-3, in both 1, 2, and
5-shot setting, achieving the second best results on 5-0 on 2 and 5-shot. We
note that methods obtaining excellent performance on VOC-SS struggle on this
scenario. In particular, SPN performances are lower than of 15% HM on average,
while incremental learning methods are not able to learn new classes properly,
performing close to standard fine-tuning. On the other hand, DWI and WI are
effective on this scenario, since they can integrate new classes without
forgetting previous knowledge. DWI is the second best method on both 1, 2, and
5-shot, being the best on the 5-0 fold. However, still outperforms it by
$0.5$% HM on 1-shot, $2.1$% HM on 2-shot, and $1.7$% HM on 5-shot. COCO. The
results for COCO-SS are reported on tables 2 (1-shot), 2 (2-shot), and 2
(5-shots). From the tables we can see that is consistently best or second best
on every shot and fold. It achieves nearly 1% HM more than the second best
method in 1-shot ($13.6$% DWI $12.8$%) and in 2-shot ($17.5$% SPN $16.5$%).
Differently, on 5-shot SPN achieves comparable performance to , $22.9$% vs
$23.0$ HM. From the detailed results on the folds we can see that while SPN
obtains better results on 20-0 and 20-1 ($+1.0$% HM and $+0.3$% HM
respectively), outperforms it on 20-2 and 20-3 ($+0.5$% and $+1.3$% HM).
Finally, we report the results for COCO-MS on tables 2 (1-shot), 2 (2-shot),
and 2 (5-shot). Also in this setting is always the best or second best method
across all shots and folds. In particular, it is the best on 1-shot on every
fold, outperforming the second best method (DWI) by 1.3% HM on average. On the
2-shot setting, is the best on 20-0 ($+1.8$% HM SPN) and 20-1 ($+1.2$% HM RT)
folds, being second best on 20-2 ($-1.2$% HM SPN) and 20-3 ($-0.8$% HM SPN).
Overall, outperforms the second best method, SPN, by $0.6$% HM. In the 5-shot
setting, the performance of and SPN are on par, achieving very similar results
for both old and new classes. In particular, comparing them on the different
folds, we see that they maintain very close performances, in terms of HM,
$22.9$% vs $23.3$% on 20-0, $22.3$% vs $21.5$% on 20-1, $26.4$% vs $26.5$% on
20-2, $29.5$% vs $29.4$% on 20-3, respectively for and SPN.

###  Prototype learning offline performance

An open question from our results is whether prototype learning in the base
step hampers the performance of the pretrained segmentation network. Ideally,
we would like the model to retain the performance of a standard semantic
segmentation model when trained offline, while keeping the advantage of
prototype learning for the FSL steps. In this section, we show that prototype
learning performs on par to other learning techniques in a standard offline
setting, when all the classes are learned in one step. To demonstrate this, in
Tab. 2 we report the results for a linear classifier trained with cross-
entropy (std), SPN [xian2019spnet], and on the base step of COCO. From the
results we can see that our model is competitive with both choices, achieving
an average mIoU of 52.1% vs 53.0% of SPN and 52.9% of standard training. Note
that while performs comparably to other approaches in the offline scenario,
the improvement on the incremental few-shot learning settings is remarkable,
as demonstrated in previous experiments.

###  Additional qualitative results

Due to space constraints, in the paper we report only the qualitative results
for VOC-SS 1-shot. Here, we expand the analysis by reporting the results for
other scenarios, COCO-SS 1-shot, COCO-SS 2-shot and VOC-SS 2-shot. Fig. 2
shows some qualitative results for different methods on COCO-SS 1-shot. From
the figure, we can see how better discriminates the new class other
approaches. Overall, we see that WI and DWI tend to assign pixels to new
classes even when they are outside the class of interest (dog second row, wc,
fourth row), while ILT and SPN may either ignore pixels of new classes
(surfboard third row) or assign them to old ones (elephant first row). On the
other hand, correctly segments both old and new classes, even in images with
clutter (surfboard third row), multiple instances (sheep last row) and complex
boundaries (dog second row). In Fig. 2 and Fig. 2 we show results for the VOC-
SS and COCO-SS 2-shot settings. Similarly to VOC-SS 1-shot and COCO-SS 1-shot,
non-finetuned methods (WI, DWI) may not discriminate new classes, when they
are similar to base ones, making incoherent predictions. Examples are cow and
bus (first and second rows of fig:qualitative-voc2) mistakenly segmented as
horse (purple) and train (light-green) respectively, and giraffe (fifth row of
fig:qualitative-coco2) segmented as zebra (blue). SPN and ILT may not properly
learn to segment new classes when trained with few complex examples. For
instance, both methods fail to segment motorcycle (third row fig:qualitative-
voc2) and sofa (fig:qualitative-coco2, fourth row), where training images are
either small and in cluttered environments (motorcycle) or mixed with other
classes (dogs in sofa). In contrast, precisely segments new classes,
discriminating them from old ones. For instance, our model correctly segments
the multiple instances of motorcycle and sheep (last row) in fig:qualitative-
voc2, while separating pixels of cat and dog from the new class sofa in
fig:qualitative-coco2. Interestingly, can correctly discriminate almost all
pixels of the new classes (cow, second row of fig:qualitative-voc2, bear and
sandwich in fig:qualitative-coco2 second and third rows), despite their
similarities (cow with horse) or large difference (bear) with old ones, or the
presence of multiple other classes (sandwich).

Method | Mean | 20-0 | 20-1 | 20-2 | 20-3
---|---|---|---|---|---
std | 52.9 | 49.8 | 52.6 | 55.1 | 54.2
SPN [xian2019spnet] | 53.0 | 49.5 | 53.2 | 54.7 | 54.6
| 52.1 | 47.9 | 51.6 | 54.4 | 54.3
Table : mIoU on base classes, before the few-shot steps, comparing the
Prototype Learning () with a standard classifier (std) and SPN [xian2019spnet]
on the COCO dataset.

!  Mean 5-0 5-1 5-2 5-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N
HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 58.3 9.7 16.7 61.9 3.2 6.1 55.2 16.3
25.2 53.6 13.1 21.1 62.4 6.3 11.5 3*[origin=c]90FSC WI [qi2018low] 62.7 15.5
24.8 66.5 10.5 18.1 58.9 21.9 31.9 58.6 15.3 24.2 66.7 14.2 23.5 DWI
[gidaris2018dynamic] 64.3 15.4 24.8 67.3 10.3 17.9 59.9 23.3 33.5 60.0 16.0
25.3 69.9 11.8 20.2 RT [tian2020rethinking] 59.1 12.1 20.1 62.7 3.8 7.1 54.5
18.7 27.9 56.3 14.6 23.2 63.1 11.4 19.3 2*[origin=c]90FSS AMP
[siam2019adaptive] 57.5 16.7 25.8 61.7 12.0 20.1 49.8 22.9 31.4 54.8 15.3 23.9
63.5 16.5 26.1 SPN [xian2019spnet] 59.8 16.3 25.6 64.1 9.0 15.8 56.2 23.9 33.5
56.2 19.3 28.7 62.7 13.1 21.7 3*[origin=c]90IL LwF [li2017learning] 61.5 10.7
18.2 63.7 2.8 5.3 59.0 19.3 29.0 59.1 14.1 22.8 64.0 6.6 11.9 ILT
[michieli2019incremental] 64.3 13.6 22.5 67.1 5.9 10.8 60.5 19.3 29.2 61.2
18.9 28.9 68.4 10.3 18.0 MiB [cermelli2020modeling] 61.0 5.2 9.7 64.6 3.1 6.0
56.9 7.6 13.4 57.3 6.3 11.4 65.4 3.9 7.4 60.9 18.6 28.4 64.4 12.7 21.2 54.3
25.1 34.3 57.1 20.3 29.9 67.6 16.2 26.1

Table : : VOC-SS 1-shot.

!  Mean 5-0 5-1 5-2 5-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N
HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 59.1 19.7 29.5 61.7 12.6 20.9 57.5
31.0 40.3 54.8 20.2 29.5 62.5 15.0 24.2 3*[origin=c]90FSC WI [qi2018low] 63.3
19.2 29.5 67.1 13.1 21.9 59.0 28.2 38.2 59.3 18.1 27.7 67.7 17.5 27.8 DWI
[gidaris2018dynamic] 64.8 19.8 30.4 68.2 15.1 24.7 60.4 30.9 40.9 60.4 17.2
26.8 70.1 16.2 26.3 RT [tian2020rethinking] 60.9 21.6 31.9 65.3 10.4 18.0 54.4
34.6 42.3 59.2 24.3 34.5 64.7 17.0 27.0 2*[origin=c]90FSS AMP
[siam2019adaptive] 54.4 18.8 27.9 59.7 12.5 20.7 44.5 28.4 34.7 53.4 17.2 26.0
59.8 17.0 26.5 SPN [xian2019spnet] 60.8 26.3 36.7 65.5 18.8 29.2 57.1 37.4
45.2 57.8 25.6 35.5 62.7 23.4 34.1 3*[origin=c]90IL LwF [li2017learning] 63.6
18.9 29.2 65.2 10.8 18.6 61.8 31.3 41.6 60.9 21.0 31.3 66.5 12.6 21.2 ILT
[michieli2019incremental] 64.2 23.1 34.0 68.4 16.1 26.1 58.3 33.7 42.7 61.1
25.6 36.1 68.9 17.1 27.4 MiB [cermelli2020modeling] 63.5 12.7 21.1 66.6 12.3
20.7 60.1 18.3 28.0 59.7 11.2 18.8 67.7 9.0 15.8 60.5 26.4 36.8 64.0 18.9 29.1
53.9 36.6 43.6 58.2 26.5 36.4 65.9 23.6 34.7

Table : : VOC-SS 2-shot.

!  Mean 5-0 5-1 5-2 5-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N
HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 55.8 29.6 38.7 58.4 22.8 32.8 52.3
42.7 47.0 50.6 29.7 37.5 62.0 23.0 33.6 3*[origin=c]90FSC WI [qi2018low] 63.3
21.7 32.3 67.5 16.3 26.3 58.7 30.8 40.4 59.4 21.3 31.4 67.5 18.4 28.9 DWI
[gidaris2018dynamic] 64.9 23.5 34.5 68.8 20.7 31.8 60.8 34.7 44.2 60.9 20.6
30.7 69.1 17.9 28.5 RT [tian2020rethinking] 60.4 27.5 37.8 65.6 19.1 29.6 55.8
38.8 45.8 55.1 29.3 38.3 65.0 22.9 33.9 2*[origin=c]90FSS AMP
[siam2019adaptive] 51.9 18.9 27.7 58.5 12.9 21.2 38.5 26.5 31.4 51.9 20.4 29.3
58.5 15.8 24.8 SPN [xian2019spnet] 58.4 33.4 42.5 63.3 28.2 39.0 53.4 43.7
48.1 54.5 33.5 41.5 62.3 28.2 38.8 3*[origin=c]90IL LwF [li2017learning] 59.7
30.9 40.8 62.8 23.9 34.6 57.1 44.0 49.7 55.9 31.6 40.3 63.0 24.4 35.2 ILT
[michieli2019incremental] 61.4 32.0 42.1 67.2 27.8 39.4 54.2 40.4 46.3 57.1
33.8 42.4 67.0 26.1 37.5 MiB [cermelli2020modeling] 65.0 28.1 39.3 68.0 24.8
36.4 62.1 35.2 44.9 60.6 27.1 37.4 69.1 25.4 37.2 60.0 33.3 42.8 64.3 26.7
37.7 53.3 41.0 46.3 57.4 33.8 42.5 65.2 31.6 42.6

Table : : VOC-SS 5-shot.

!  Mean 5-0 5-1 5-2 5-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N
HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 47.2 3.9 7.2 46.8 2.0 3.8 42.0 8.0
13.4 47.3 3.5 6.5 52.7 2.1 4.0 3*[origin=c]90FSC WI [qi2018low] 66.6 16.1 25.9
68.8 14.9 24.5 63.5 24.4 35.3 63.3 14.3 23.4 70.9 10.6 18.5 DWI
[gidaris2018dynamic] 67.2 16.3 26.2 69.0 15.7 25.6 63.6 25.8 36.7 64.1 13.6
22.5 71.9 10.0 17.6 RT [tian2020rethinking] 49.2 5.8 10.4 45.4 2.2 4.2 41.5
12.4 19.1 46.7 4.8 8.6 53.3 4.5 8.2 2*[origin=c]90FSS AMP [siam2019adaptive]
58.6 14.5 23.2 61.6 12.1 20.2 54.5 22.8 32.1 56.1 11.8 19.5 62.4 11.3 19.1 SPN
[xian2019spnet] 49.8 8.1 13.9 48.7 3.6 6.6 44.0 13.7 20.9 51.4 8.7 14.9 55.0
6.5 11.7 3*[origin=c]90IL LwF [li2017learning] 42.1 3.3 6.2 42.1 1.8 3.4 37.9
6.8 11.5 41.4 2.5 4.7 47.1 2.2 4.3 ILT [michieli2019incremental] 43.7 3.3 6.1
42.3 1.7 3.3 41.0 6.1 10.6 41.9 3.5 6.4 49.6 1.8 3.4 MiB
[cermelli2020modeling] 43.9 2.6 4.9 41.0 1.0 2.0 40.2 5.8 10.2 43.4 2.4 4.5
51.0 1.1 2.2 64.1 16.9 26.7 67.6 13.3 22.3 58.0 27.1 36.9 61.0 15.6 24.9 69.8
11.5 19.8

Table : : VOC-MS 1-shot.

!  Mean 5-0 5-1 5-2 5-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N
HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 53.5 4.4 8.1 54.5 2.3 4.5 51.2 8.9
15.2 51.5 4.3 7.9 56.9 2.2 4.3 3*[origin=c]90FSC WI [qi2018low] 66.6 19.8 30.5
69.1 17.1 27.4 63.7 31.9 42.5 63.5 16.7 26.5 70.3 13.5 22.7 DWI
[gidaris2018dynamic] 67.5 21.6 32.7 69.7 21.3 32.6 63.9 35.4 45.6 64.3 15.8
25.4 72.0 14.0 23.5 RT [tian2020rethinking] 36.0 4.9 8.6 43.8 3.1 5.8 23.0 7.2
11.0 28.3 5.3 9.0 40.7 4.7 8.5 2*[origin=c]90FSS AMP [siam2019adaptive] 58.4
16.3 25.5 62.3 12.3 20.6 54.0 27.2 36.1 55.9 14.7 23.3 61.5 10.8 18.4 SPN
[xian2019spnet] 56.4 10.4 17.6 58.3 5.8 10.6 54.0 18.2 27.2 55.6 10.4 17.5
57.8 7.3 13.0 3*[origin=c]90IL LwF [li2017learning] 51.6 3.9 7.3 50.7 1.9 3.7
49.4 8.6 14.7 50.4 3.4 6.3 55.9 1.7 3.4 ILT [michieli2019incremental] 52.2 4.4
8.1 50.7 2.0 3.9 51.6 9.7 16.3 49.9 4.0 7.4 56.8 1.7 3.4 MiB
[cermelli2020modeling] 51.9 2.1 4.0 52.7 1.7 3.4 47.9 3.4 6.4 50.6 2.5 4.7
56.4 0.8 1.5 65.2 23.7 34.8 68.0 17.7 28.1 61.3 40.4 48.7 62.5 21.8 32.4 69.2
15.0 24.7

Table : : VOC-MS 2-shot.

!  Mean 5-0 5-1 5-2 5-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N
HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 58.7 7.7 13.6 59.9 5.1 9.5 57.0 14.8
23.5 55.9 7.1 12.5 62.5 3.7 7.0 3*[origin=c]90FSC WI [qi2018low] 66.6 21.9
33.0 69.6 20.4 31.5 63.4 32.9 43.3 63.4 20.1 30.6 69.9 14.3 23.8 DWI
[gidaris2018dynamic] 67.6 25.4 36.9 70.3 27.9 39.9 64.1 38.0 47.7 64.5 19.6
30.1 71.5 16.0 26.1 RT [tian2020rethinking] 45.1 10.0 16.4 52.5 7.3 12.7 35.4
16.7 22.7 39.4 9.3 15.0 53.1 6.9 12.2 2*[origin=c]90FSS AMP [siam2019adaptive]
57.1 17.2 26.4 62.5 13.5 22.2 50.1 25.3 33.7 55.1 18.7 28.0 60.8 11.2 18.9 SPN
[xian2019spnet] 61.6 16.3 25.8 62.3 10.4 17.9 59.5 27.6 37.7 60.3 14.7 23.7
64.2 12.3 20.6 3*[origin=c]90IL LwF [li2017learning] 59.8 7.5 13.4 60.7 5.2
9.6 57.8 14.6 23.3 57.4 6.8 12.2 63.3 3.5 6.6 ILT [michieli2019incremental]
59.0 7.9 13.9 59.9 5.4 9.8 57.0 15.0 23.8 56.0 7.1 12.6 62.9 4.0 7.6 MiB
[cermelli2020modeling] 60.9 5.8 10.5 61.0 4.8 8.9 58.4 9.6 16.5 59.5 5.9 10.8
64.9 2.7 5.2 64.5 27.5 38.6 67.4 23.8 35.2 60.4 41.6 49.3 61.6 25.4 35.9 68.6
19.0 29.8

Table : : VOC-MS 5-shot.

!  Mean 20-0 20-1 20-2 20-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 41.2 4.1 7.5 36.2 2.0 3.8 40.6
4.9 8.7 45.0 3.7 6.8 43.1 5.8 10.3 3*[origin=c]90FSC WI [qi2018low] 43.8 6.9
11.9 41.0 4.8 8.6 42.8 7.8 13.2 46.4 6.8 11.8 45.1 8.1 13.8 DWI
[gidaris2018dynamic] 44.5 7.5 12.8 39.8 5.0 8.9 44.4 8.2 13.9 47.2 6.8 11.9
46.6 9.9 16.4 RT [tian2020rethinking] 46.2 5.8 10.2 39.4 3.4 6.2 46.5 6.1 10.8
50.4 5.3 9.6 48.4 8.2 14.1 2*[origin=c]90FSS AMP [siam2019adaptive] 37.5 7.4
12.4 33.4 4.8 8.4 37.4 8.4 13.8 39.9 8.7 14.2 39.1 7.8 13.0 SPN
[xian2019spnet] 43.5 6.7 11.7 39.2 4.6 8.2 43.7 6.4 11.1 46.8 7.1 12.3 44.3
8.9 14.8 3*[origin=c]90IL LwF [li2017learning] 43.9 3.8 7.0 37.8 1.8 3.4 43.7
4.3 7.9 47.9 3.7 6.8 46.1 5.4 9.6 ILT [michieli2019incremental] 46.2 4.4 8.0
40.7 2.4 4.5 46.0 4.4 8.1 50.3 4.7 8.6 47.8 6.0 10.6 MiB
[cermelli2020modeling] 43.8 3.5 6.5 37.5 2.1 4.0 44.1 3.6 6.6 47.6 3.9 7.1
46.0 4.4 8.1 40.8 8.2 13.6 38.6 5.4 9.5 39.7 8.6 14.2 43.5 7.7 13.1 41.4 10.9
17.2

Table : : COCO-SS 1-shot.

!  Mean 20-0 20-1 20-2 20-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 41.5 7.3 12.4 37.4 4.2 7.6 40.3
9.0 14.7 45.4 7.7 13.2 43.1 8.4 14.0 3*[origin=c]90FSC WI [qi2018low] 44.2 7.9
13.5 41.8 5.2 9.2 43.3 9.8 16.0 46.8 7.6 13.1 44.7 9.2 15.3 DWI
[gidaris2018dynamic] 45.0 9.4 15.6 40.4 6.1 10.6 45.2 10.7 17.4 47.4 9.1 15.3
46.9 11.8 18.8 RT [tian2020rethinking] 46.7 8.8 14.8 40.6 5.5 9.7 46.8 10.5
17.2 50.8 8.1 14.0 48.5 11.1 18.1 2*[origin=c]90FSS AMP [siam2019adaptive]
35.7 8.8 14.2 30.9 5.8 9.8 36.2 10.5 16.3 38.4 9.2 14.8 37.3 9.9 15.6 SPN
[xian2019spnet] 43.7 10.2 16.5 40.0 6.7 11.5 43.3 11.5 18.1 47.0 10.7 17.5
44.6 11.9 18.7 3*[origin=c]90IL LwF [li2017learning] 44.3 7.1 12.3 39.2 4.5
8.0 43.8 8.7 14.5 48.1 7.3 12.7 46.0 8.0 13.6 ILT [michieli2019incremental]
46.3 6.5 11.5 40.5 4.5 8.1 46.3 7.1 12.3 50.4 6.7 11.8 48.1 7.9 13.5 MiB
[cermelli2020modeling] 44.4 6.0 10.6 38.2 4.2 7.6 44.5 7.1 12.3 48.6 6.5 11.4
46.3 6.1 10.8 40.9 11.1 17.5 38.6 6.8 11.6 39.4 13.1 19.7 43.5 11.4 18.1 42.2
13.1 20.0

Table : : COCO-SS 2-shot.

!  Mean 20-0 20-1 20-2 20-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 41.6 12.3 19.0 37.3 7.6 12.6
40.9 15.0 22.0 45.3 13.7 21.0 43.0 12.9 19.8 3*[origin=c]90FSC WI [qi2018low]
43.6 8.7 14.6 41.7 6.0 10.5 42.8 10.7 17.1 45.7 8.6 14.4 44.4 9.7 15.9 DWI
[gidaris2018dynamic] 44.9 12.1 19.1 40.5 8.2 13.6 45.3 14.4 21.9 47.0 12.2
19.4 46.7 13.7 21.2 RT [tian2020rethinking] 46.9 13.7 21.2 41.1 9.5 15.4 46.4
15.9 23.7 50.7 13.8 21.6 49.1 15.7 23.8 2*[origin=c]90FSS AMP
[siam2019adaptive] 34.6 11.0 16.7 31.2 7.2 11.6 34.8 14.5 20.4 36.9 10.9 16.8
35.6 11.5 17.4 SPN [xian2019spnet] 43.7 15.6 22.9 40.1 11.5 17.9 42.9 17.7
25.1 46.4 16.4 24.2 45.4 16.6 24.4 3*[origin=c]90IL LwF [li2017learning] 44.6
12.9 20.1 39.6 8.0 13.3 43.7 15.9 23.3 48.6 14.1 21.9 46.4 13.8 21.2 ILT
[michieli2019incremental] 47.0 11.0 17.8 41.9 7.1 12.2 47.0 13.9 21.5 50.4
11.2 18.3 48.6 11.8 19.0 MiB [cermelli2020modeling] 44.7 11.9 18.8 38.2 8.1
13.4 44.9 13.9 21.2 49.0 13.4 21.0 46.7 12.2 19.3 42.8 15.7 23.0 40.6 10.7
16.9 41.5 17.7 24.8 45.3 16.9 24.7 43.9 17.5 25.0

Table : : COCO-SS 5-shot.

!  Mean 20-0 20-1 20-2 20-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 38.5 4.8 8.6 33.3 3.8 6.8 39.8
3.6 6.6 40.3 4.1 7.5 40.5 7.8 13.1 3*[origin=c]90FSC WI [qi2018low] 46.3 8.3
14.0 42.6 5.6 9.9 45.9 9.1 15.2 48.9 8.1 13.8 47.9 10.3 17.0 DWI
[gidaris2018dynamic] 46.2 9.2 15.3 41.0 5.7 9.9 46.5 9.7 16.0 48.8 8.6 14.7
48.6 12.7 20.1 RT [tian2020rethinking] 38.4 5.2 9.1 34.4 2.5 4.6 42.2 5.7 10.1
45.1 6.0 10.6 31.8 6.4 10.7 2*[origin=c]90FSS AMP [siam2019adaptive] 36.6 7.9
13.1 34.0 6.7 11.2 36.7 8.0 13.2 38.5 8.2 13.6 37.2 8.8 14.2 SPN
[xian2019spnet] 40.3 8.7 14.3 37.1 7.5 12.4 41.1 7.0 11.9 42.3 8.2 13.7 40.6
12.2 18.8 3*[origin=c]90IL LwF [li2017learning] 41.0 4.1 7.4 35.5 3.3 6.0 42.4
2.7 5.2 42.9 3.8 6.9 43.1 6.6 11.4 ILT [michieli2019incremental] 43.7 6.2 10.8
38.5 4.8 8.5 45.0 4.8 8.7 45.8 5.2 9.4 45.5 10.0 16.4 MiB
[cermelli2020modeling] 40.4 3.1 5.8 32.8 1.2 2.4 41.5 2.3 4.4 43.9 3.9 7.1
43.4 5.0 9.0 40.4 10.4 16.6 37.0 8.3 13.6 40.5 10.0 16.0 42.0 9.1 15.0 42.3
14.3 21.4

Table : : COCO-MS 1-shot.

!  Mean 20-0 20-1 20-2 20-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 40.3 6.8 11.7 36.4 5.5 9.5 40.4
5.1 9.0 42.5 6.0 10.6 41.8 10.8 17.1 3*[origin=c]90FSC WI [qi2018low] 46.5 9.3
15.4 43.2 5.8 10.3 46.1 10.0 16.4 49.1 9.5 15.9 47.5 11.7 18.8 DWI
[gidaris2018dynamic] 46.5 11.4 18.3 35.8 7.1 11.8 39.4 9.3 15.0 41.2 9.4 15.4
43.1 7.8 13.3 RT [tian2020rethinking] 43.8 10.1 16.4 38.6 5.6 9.8 44.3 11.3
18.0 47.0 8.7 14.6 45.2 14.7 22.2 2*[origin=c]90FSS AMP [siam2019adaptive]
36.0 9.2 14.6 33.2 7.9 12.8 36.7 8.9 14.3 37.9 8.7 14.2 36.4 11.2 17.2 SPN
[xian2019spnet] 41.7 12.5 19.2 38.4 8.8 14.3 41.9 10.1 16.3 44.0 13.6 20.8
42.5 17.3 24.6 3*[origin=c]90IL LwF [li2017learning] 42.7 6.5 11.3 38.1 6.3
10.9 43.3 4.9 8.8 44.6 5.6 9.9 44.7 9.4 15.5 ILT [michieli2019incremental]
47.1 10.0 16.5 46.5 7.1 12.3 48.5 8.0 13.7 47.7 13.2 20.7 45.7 11.9 18.8 MiB
[cermelli2020modeling] 42.7 5.2 9.3 36.9 3.4 6.3 43.6 4.0 7.3 45.6 5.9 10.4
44.8 7.5 12.8 40.1 13.1 19.8 37.2 10.3 16.1 39.9 12.7 19.2 42.4 12.7 19.6 41.0
16.8 23.8

Table : : COCO-MS 2-shot.

!  Mean 20-0 20-1 20-2 20-3 Method mIoU-B mIoU-N HM mIoU-B mIoU-N HM mIoU-B
mIoU-N HM mIoU-B mIoU-N HM mIoU-B mIoU-N HM FT 39.5 11.5 17.8 36.1 11.1 17.0
38.4 9.3 15.0 42.0 10.6 16.9 41.5 14.8 21.8 3*[origin=c]90FSC WI [qi2018low]
46.3 10.3 16.8 43.4 7.3 12.4 45.7 11.1 17.8 48.7 10.5 17.2 47.4 12.3 19.5 DWI
[gidaris2018dynamic] 46.6 14.5 22.1 35.8 7.1 11.8 39.4 9.3 15.0 41.2 9.4 15.4
43.1 7.8 13.3 RT [tian2020rethinking] 44.1 16.0 23.5 39.8 11.8 18.3 44.1 16.2
23.7 47.0 15.7 23.6 45.4 20.2 28.0 2*[origin=c]90FSS AMP [siam2019adaptive]
33.2 11.0 16.5 30.8 9.3 14.2 33.5 11.6 17.3 34.5 10.2 15.8 34.0 12.9 18.7 SPN
[xian2019spnet] 41.4 18.2 25.3 37.9 16.8 23.3 41.0 14.6 21.5 44.0 19.0 26.5
42.8 22.4 29.4 3*[origin=c]90IL LwF [li2017learning] 42.3 12.6 19.4 38.7 12.3
18.6 41.6 10.6 16.9 44.7 11.5 18.2 44.4 16.1 23.6 ILT
[michieli2019incremental] 45.3 15.3 22.8 41.1 14.5 21.4 45.3 13.7 21.0 47.8
14.1 21.8 47.1 18.8 26.9 MiB [cermelli2020modeling] 43.8 11.5 18.2 38.3 10.1
16.0 44.4 9.4 15.6 46.4 11.4 18.4 46.2 14.8 22.4 41.1 18.3 25.3 38.2 16.3 22.9
39.7 15.5 22.3 43.7 18.9 26.4 42.7 22.6 29.5

Table : : COCO-MS 5-shot.

[width=0.98]fig/qual_coco.pdf

Figure : Qualitative results on the COCO-SS 1-shot setting.

[width=0.98]fig/qual_voc2.pdf

Figure : Qualitative results on the VOC-SS 2-shot setting.

[width=0.98]fig/qual_coco2.pdf

Figure : Qualitative results on the COCO-SS 2-shot setting.