In interactive object segmentation a human collaborates with a Computer Vision model to segment an object of interest [12, 46, 52, 11]. The process iteratively alternates between the user providing corrections on the current segmentation and the model refining the segmentation based on these corrections. The objective of the model is to infer the right segmentation from as few corrections as possible (typically point clicks [8, 15] or strokes [46, 24] on mislabeled pixels). This enables fast and accurate object segmentation, which is indispensable for both image editing  and collecting ground-truth segmentation masks at scale .
State-of-the-art methods train a convolutional neural network (CNNs) which takes the image and the user corrections as input and predicts a foreground/background segmentation [52, 33, 10, 35, 31, 11, 28]. At test time, the model parameters are fixed and corrections are used as additional input to guide the model predictions. But in fact, user corrections directly specify the ground-truth labelling of the corrected pixels. In this paper we capitalize on this observation. We treat user corrections as training examples to adapt our model on-the-fly to the data at hand. We do this in two ways: (1) in single image adaptation we iteratively adapt model parameters to a specific image; (2) in image sequence adaptation we adapt model parameters to a sequence of images. Each of these leads to distinct advantages over using a fixed model.
During single image adaptation our model learns the specific appearance of the current object instance and the surrounding background. This allows it to adapt even to subtle differences between foreground and background for that specific example. This is necessary when the image has objects which have similar color as the background (Fig. 1a), has blurry object boundaries, or has low contrast. Importantly, image adaptation enables user corrections to have non-local effects. For example, when marking something to be background, this information will be propagated to distant regions in the image with the same appearance. Finally, a fixed model can sometimes ignore the user corrections and overrule them in its next prediction. We avoid this undesired behavior by updating the model parameters until its predictions respect the user corrections.
During image sequence adaptation we continuously adapt the model to a new sequence of images, thus optimizing its parameters for their distribution, which might be different than the training one. We can also adapt to a new object class distribution. An important case is adaptation to a set of new classes unseen during training. Another is specialization to a single class, useful for collecting many examples in high-precision domains, such as segmentations of pedestrians for self-driving car applications. Fig. 1b shows an example of specializing to the single, unseen class donut. Finally, our idea of image sequence adaptation also enables to handle large domain changes, where the imaging modality changes between training and testing. We demonstrate this by training on consumer photos while testing on medical and aerial images (Fig. 1c). Naturally, single image adaptation and image sequence adaptation can be used jointly, leading to a method that combines their advantages.
In summary: we treat user corrections as training examples to adapt our segmentation model on-the-fly to the data at hand. We evaluate our method on 8 diverse datasets. On all it shows reductions in corrections necessary to achieve a target quality, compared to a fixed model. With single image adaptation we reduce the number of clicks by 20% on unseen classes of COCO . With image sequence adaptation we bridge large domain changes: when trained on consumer photos (PASCAL VOC ) and tested on aerial imagery (Rooftop ) we reduce the number of clicks by 60%. With the combination we achieve state-of-the-art on four standard interactive segmentation datasets: PASCAL VOC12 , GrabCut , DAVIS  and Berkeley .
2 Related Work
Interactive Object Segmentation. Traditional methods model interactive segmentation as an energy minimization problem on a graph defined over pixels [12, 46, 7, 24, 41]. User inputs are used to create an image-specific appearance model based on low-level features (e.g
. color), which is then used to predict foreground and background probabilities. A pairwise smoothness term between neighboring pixels encourages regular segmentation outputs. Hence these classical methods are based on a weak appearance model which is specialized to one specific image.
Recent methods rely on Convolutional Neural Networks (CNNs) to interactively produce a segmentation mask [52, 33, 10, 35, 31, 15, 27, 28, 3]. These methods take the image and user corrections (transformed into a guidance map) as input and map them to foreground and background probabilities. This mapping is optimized over a training dataset and remains fixed at test time. Hence these models have a strong appearance model but it is not optimized for the image or dataset at hand.
Our method combines the advantages of traditional and recent approaches: We use a modern CNN to learn a strong initial appearance model from a training set. During segmentation of a new test image, we adapt the model to it. It thus learns an appearance model of foreground and background specifically for that image. Furthermore, we also continuously adapt the model to the new image and class distribution of the test set, which may be far from what the model is originally trained on.
Gradient Descent during Inference. Several methods iteratively minimize a loss during inference time. The concurrent work of  uses self-supervision to adapt the feature extractor of a multi-tasking model to the test distribution. Instead, we directly adapt the full model by minimizing the task loss. Others iteratively update the inputs of a model [23, 25, 28], e.g. for style transfer . In the domain of interactive segmentation,  updates the guidance map which encodes the user corrections and is input to the model. Instead our method updates the model parameters, making it more general and allowing it to adapt to individual images as well as sequences.
In-domain Fine-Tuning. In other applications it is common practice to fine-tune on in-domain data when transferring a model to a new domain [13, 39, 51, 57]. For example, when supervision for the first frame of a test video is available [40, 51, 13], or after annotating a subset of an image dataset [39, 57]. In interactive segmentation the only existing attempt is , which performs polygon annotation. However, (1) it does not consider adapting to a particular image; (2) their process to fine-tune on a dataset involves 3 different models, so they do it only a few times per dataset; (3) they cannot directly train on user corrections, only on complete masks from previous images; (4) finally, they require a bounding box on the object as input.
Few-shot and Continual Learning. Our method automatically adapts to distribution shifts and domain changes. It performs domain adaptation from limited supervision, similar to few-shot learning [43, 22, 48, 42]. It also relates to continual learning [44, 21]
, except that the output label space of the classifier is fixed. As in other work, our method needs to balance preserving existing knowledge and adapting to the new data. This is often done by fine-tuning on new tasks while discouraging large changes in the network parameters, either by penalizing changes to important parameters[30, 54, 5, 6] or penalizing changing predictions of the model on old tasks [32, 47, 38]. Alternatively, some training data of the old task is kept and the model is trained on a mixture of the old and new task data [44, 9].
We adopt a typical interactive object segmentation process [12, 52, 35, 31, 11, 28]: the model is given an image and makes an initial foreground/background prediction for every pixel. The prediction is then overlaid on the image and presented to the user, who is asked to make a correction. The user clicks on a single pixel to mark that it was incorrectly predicted to be foreground instead of background or vice versa. The model then updates the predicted segmentation based on all corrections received so far. This process iterates until the segmentation reaches a desired quality level.
We start by describing the model we build on (Sec. 3.1). Then, we describe our core contribution: treating user corrections as training examples at test-time to adapt our model on-the-fly (Sec. 3.2). Lastly, we describe how we simulate user corrections to train and test our method (Sec. 3.3).
3.1 Initial Segmentation Model
We use a standard interactive segmentation model  as the basis of our method. The model takes an RGB image and the user corrections as input and produces a segmentation mask. As in  we encode the position of user corrections by placing binary disks into a two-channel map with the same resolution as the image (one channel for foreground and one for background corrections). The user corrections are concatenated with the RGB image to form a 5-channel map which is provided as input to the network.
As network architecture we use DeepLabV3+ , which has demonstrated excellent performance on semantic segmentation. It modifies a strong image classification network  for dense pixel-level prediction by incorporating multi-scale context via Atrous Spatial Pyramid Pooling  and a decoder to increase the output spatial resolution. However, we note that our method does not depend on a specific architecture and can be used with others as well.
For training the model we need a training dataset with ground-truth object segmentations, as well as user corrections which we simulate (Sec. 3.3). We train the model using the cross-entropy loss over all pixels in an image:
where is the 5-channel input defined above (image plus corrections), are the pixel labels for the ground-truth object segmentations, and represents the mapping of the convolutional network parameterized by . denotes the norm.
We produce the initial parameters of the segmentation model by minimizing
over the training set using stochastic gradient descent.
3.2 Learning from Corrections at Test-Time
In previous interactive object segmentation works all model parameters are fixed at test time [52, 10, 35, 31, 11, 28]. Corrections are only used as inputs to guide the predictions. Instead, we treat corrections as ground-truth labels to adapt the model at test time. We achieve this by minimizing the cross-entropy loss over the corrected pixels:
where in an indicator function and
is a vector of values, indicating what pixels were corrected to what label. Pixels that were corrected to be positive are set to and negative pixels to . The remaining ones are set to , so that they are ignored in the loss. As there are very few corrections available at test time, this loss is computed over a sparse set of pixels. This is in contrast to the initial training which had supervision at every pixel (Sec. 3.1). Hence, special care needs to be taken to make this form of supervision useful in practice, as we discuss next. We illustrate the contrast between the two forms of supervision in Fig. 2.
Regularization. We propose two forms of regularization. First, we regularize the model by treating the initial mask prediction as ground-truth and making it a target in the cross-entropy loss, i.e. . Having this loss prevents the model from focusing only on the user corrections while forgetting the initially good predictions on pixels for which no corrections were given.
Second, we regularize the network to prevent it from forgetting patterns learned on the initial training set, such as object shape priors. Specifically, we add a cost for changing important network parameters [30, 54, 5]:
where are the initial model parameters, are the updated parameters and is the importance of each parameter. is the element-wise square (Hadamard square). Intuitively, this loss penalizes changing the network parameters away from their initial values, where the penalty is higher for important parameters. We compute
using Memory-Aware Synapses (MAS)
, which estimates importance based on how much changes to the parameters affect the prediction of the model.
Combined loss. Our full method uses a linear combination of the above losses:
where balances the importance of the user corrections vs. the predicted mask, and defines the strength of parameter regularization. Next, we introduce single image adaptation and image sequence adaptation, which both minimize Eq. (4). Their difference lies in how the model parameters are updated: individually for each image or over a sequence.
3.2.1 Adapting to a single image
We adapt the segmentation model to a particular image by training on the click corrections. We start from the segmentation model with parameters fit to the initial training set (Sec. 3.1). Then we update them by running several gradient descent steps to minimize (4) every time the user makes a correction (Algo. 1). We choose the learning rate and the number of update steps such that the updated model respects the user corrections. This effectively turns corrections into constraints. This process results in a segmentation mask , predicted using the updated parameters .
Adapting the initial model to the current test image brings two core advantages. First, it learns about the specific appearance of the object and background in the current image. Hence corrections have a larger impact and can also improve the segmentation of distant image regions which have similar appearance. The model can also adapt to low-level photometric properties of this image, such as overall illumination, blur, and noise, which results in better segmentation in general. Second, our adaptation step makes the corrections effectively hard constraints, so the model will preserve the corrected labeling in later iterations too.
This adaptation is done for each image separately, and the updated is discarded once an object is segmented.
3.2.2 Adapting to an image sequence
Here we describe how to continuously adapt the segmentation model to a sequence of test images using an online algorithm. Again, we start from the model parameters fit to the initial training set (sec. 3.1). When the first test image arrives, we perform interactive segmentation using these initial parameters. Then, after segmenting each image , the model parameters are updated to by doing a single gradient descent step to minimize Eq. (4) for that image. The updated model parameters are used to segment the next image .
If all corrections were used equally in the loss and guidance, the model could eventually degrade to simply using the guidance information as a prediction, without relying on image appearance. We avoid this by subsampling the clicks given to the network as guidance, but using all clicks to compute the adaptation loss (Eq. (4)). This forces the network to rely on appearance for propagating the corrections to the rest of the image, where the loss is sparsely evaluated at the pixel locations which were corrected.
Through the method described above our model adapts to the whole test image sequence, but does so gradually, as images arrive in the sequence. As a consequence the process is fast, does not require storing a growing number of images, and can be used in a online setting. In this fashion it can adapt to changing appearance properties, adapt to unseen classes, and specialize to one particular class. It can even adapt to radically different image domains as we demonstrate in Sec. 4.3.
3.2.3 Combined adaptation
The two types of adaptation described above can easily be combined. For a test image , we segment the object using the image adaptation method of Sec. 3.2.1 (Algo. 1). After segmenting a test image, we gather all corrections provided for that image and apply a image sequence adaptation step to update the model parameters to (Sec. 3.2.2). At the next image, the image adaptation process will thus start from parameters better suited for the test sequence. This combination allows to leverage the distinct advantages of the two types of adaptation.
3.3 Simulating user corrections
Test-time corrections. When interactively segmenting an object, the user clicks on a mistake in the model prediction. To simulate this we follow [52, 10, 35], which assumes that the user clicks on the largest error region. We obtain this error region by comparing the model predictions with the ground-truth and select its center pixel.
Train-time corrections. Ideally one wants to train with the same user model that is used at test-time. To make this computationally feasible, we train the model in two stages .
First, we sample corrections using ground-truth object segmentations [10, 28, 31, 33, 52]. Positive user corrections are sampled uniformly at random on the object. Negative user corrections are sampled according to three strategies: (1) uniformly at random from pixels around the object, (2) uniformly at random on other objects, and (3) uniformly around the object. We use these corrections to train the model until convergence.
Then, we continue training by iteratively sampling corrections . For each image we keep a set of user corrections . Given we predict a segmentation mask, simulate the next user correction (as done at test time), and add it to . Based on this additional correction, we predict a new segmentation mask and minimize the loss (Eq. (1)). Initially, corresponds to the corrections simulated in the first stage, and over time more user corrections are added. As we want the model to work well even with few user corrections, we thus periodically reset to the initial clicks.
We extensively evaluate our single image adaptation and image sequence adaptation methods on several standard datasets as well as on aerial and medical images. These correspond to increasingly challenging adaptation scenarios.
Adaptation scenarios. We first consider distribution shift, where the training and test image sets come from the same general domain, consumer photos, but differ in their image and object statistics (Sec. 4.1). This includes differences in image complexity, object size distribution, and when the test set contains object classes absent during training. Then, we consider a class specialization scenario, where a sequence of objects of a single class has to be segmented (Sec. 4.2). Finally we test how our method handles large domain changes where the imaging modality changes between training and testing. We demonstrate this by going from consumer photos to aerial and medical images (Sec. 4.3).
Initial segmentation model. All experiments start from the model of Sec. 3.1, trained on PASCAL VOC12  augmented with SBD  (10582 images with 24125 segmented instances of 20 object classes). This initial model already achieves state-of-the-art results on the PASCAL VOC12 validation set, simply by increasing the encoder resolution compared to  (Tab. 4
). This shows that models with fixed parameters perform well when the train and test probability distributions match. When testing our adaptation method this model serves as our point of reference.
Evaluation metrics. We evaluate with two standard metrics [52, 33, 10, 35, 31, 11, 28]: (1) IoU@k, the average intersection-over-union between the ground-truth and predicted segmentation masks, given corrections per image, and (2) clicks@q%, the average number of corrections needed to reach an IoU of on every image (thresholded at 20 clicks).
We optimize the hyperparameters for both single image adaptation and image sequence adaptation on a subset of the ADE20k dataset[55, 56]. Hence, the hyperparameters are optimized for adapting from PASCAL VOC12 to ADE20k, which is distinct from the distribution shifts and domain changes we evaluate on.
4.1 Adapting to distribution shift
We test how well we can adapt the initial model trained on PASCAL VOC12 to other consumer photos datasets.
Datasets. We test on: (1) Berkeley , 100 images with a single foreground object. (2) YouTube-VOS , a large video object segmentation dataset. We use the test set of the 2019 challenge, where we take the first frame with ground truth (1169 objects). (3) COCO , a large segmentation dataset with 80 object classes. 20 of those overlap with the ones in the PASCAL VOC12 dataset and are thus seen during training. The other 60 are unseen. We sample 10 objects per class from the validation set and separately report results for seen (200 objects) and unseen classes (600 objects) as in [52, 36]. We also study how image sequence adaptation behaves on longer sequences of 100 objects for each unseen class (named COCO unseen 6k).
Results. We report our results in Tab. 1 and in Fig. 3. Both types of adaptation improve performance on all the tested datasets. On the first few user corrections the single image adaptation (IA) model performs similarly to the initial model as it is initialized with the same parameters. But as more corrections are provided, it uses these more effectively to adapt its appearance model to a specific image. Thus, it performs particularly well in the high-click regime, which is especially useful for objects that are challenging to segment (e.g. due to low illumination, Fig. 4), or when very accurate masks are desired.
During image sequence adaptation (SA), the model adapts to the test image distribution and thus learns to produce good segmentation masks given just a few clicks (Fig. 2(a)). As a result, SA outperforms the initial model on all datasets with distribution shifts (Tab. 1). By adapting from images to the video frames of YouTube-VOS, SA reduces the clicks needed to reach 85% IoU by more than 20%. Importantly, we find that our method adapts fast, making a real difference after just a few images, and then it keeps on improving even as the test sequence becomes thousands of images long (Fig. 2(b)). This translates to a large improvement given a fixed budget of 4 clicks per object: on the COCO unseen 6k split it achieves 69% IoU compared to the 57% of the initial model without adaptation (Fig. 2(a)).
Generally, the curves for image sequence adaptation grow faster in the low click regime than the single image adaptation ones, but then exhibit stronger diminishing returns in the higher click regime (Fig. 2(a)). Hence, combining the two compounds their advantages leading to a method that considerably improves over the initial model on the full range of number of corrections and sequence lengths (Fig. 2(a)). Compared to the fixed model, our combined method significantly reduces the number of clicks needed to reach the target accuracy on all datasets: from a 9% reduction on Berkeley and COCO seen, to a 30% reduction on COCO unseen 6k.
4.2 Adapting to a specific class
When a user segments objects of a single class at test-time, image sequence adaptation naturally specializes its appearance model to that class. We evaluate this phenomenon on 4 COCO classes. We form 4 test image sequences, each focusing on a single class, containing objects of varied appearance. The classes are selected based on how image sequence adaptation compared to the initial model in Sec. 4.1: (1) donut (2540 objects), the class that improved most, (2) bench (3500), which shows average improvement, (3) umbrella (3979), which does not improve significantly, and (4) bed (1450), one of the few classes where performance degrades slightly.
Results. Tab. 2, Fig. 2(c) present results. The class specialization brought by our image sequence adaptation (SA) leads to good masks from very few clicks. For example, on the donut class it reduces clicks@85% by 39% compared to the initial model (Tab. 2). Given just 2 clicks, it reaches 66% IoU for that class, compared to 25% IoU for the initial model (Fig. 2(c)). The results for the other classes follow a similar pattern, showing that image sequence adaptation learns an effective appearance model for a single class.
|clicks @ 85% IoU|
4.3 Adapting to domain changes
|DRIONS-DB ||Rooftop |
|Medical Dataset||Aerial Dataset|
|Method||clicks@90% IoU||clicks@80% IoU|
We now test our method’s capabilities of adapting to domain changes by evaluating on aerial and medical imagery.
Datasets. We explore two test datasets: (1) Rooftop Aerial , a dataset of 65 aerial images with segmented rooftops and (2) DRIONS-DB , a dataset of 110 retinal images with a segmentation of the optic disc of the eye fundus. We use the masks of the first expert. Importantly, the initial model is trained on PASCAL VOC12 and adaptation is tuned for ADE20k, both consumer photos datasets. Hence, we explore truly large domain changes here.
Results. Both our forms of adaptation significantly improve over the initial model (Tab. 3, Fig. 5). Single image adaptation can only adapt to a limited extent, as it independently adapts to individual images starting from the initial model every time. Nonetheless, it offers a significant improvement, reducing the number of clicks needed to reach the desired IoU by 14%-29%. Image sequence adaptation (SA) shows extremely strong performance, as its adaptation effects accumulate over the duration of the test sequence. It reduces the needed user input by 60% for the Rooftop Aerial dataset and by over 70% for DRIONS-DB. This improvement is even larger when combining the two types of adaptation (Tab. 3). Importantly, we find that our method adapts fast: on DRIONS-DB clicks@90% drops quickly and converges to just 2 corrections, as the length of the test sequence increases (Fig. 4(a)). In contrast, the initial model performs poorly on both datasets. On the Rooftop Aerial dataset, it needs even more clicks than there are points in the ground truth polygons (8.9 vs. 5.1). This shows that even a state-of-the-art model like our initial model fails to generalize to truly different domains and highlights the importance of adaptation.
To summarize: We show that our method can bridge large domain changes spanning varied datasets and sequence lengths. With a simple update scheme of doing a single gradient descent step per image, our image sequence adaptation successfully addresses a major shortcoming of neural networks, for the case of interactive segmentation: Their poor generalization to changing distributions [45, 4].
4.4 Comparison to State-of-the-Art
|VOC12 ||GrabCut ||Berkeley ||DAVIS ||COCO |
|validation||10% of frames||seen||unseen||average|
|Method / clicks@q%||85% IoU||90% IoU||90% IoU||85% IoU||85% IoU||85% IoU||85% IoU|
|iFCN w/ GraphCut ||6.88||6.04||8.65||-||8.31||7.82||7.94|
|Latent Diversity ||-||4.79||-||5.95||-||-||7.86|
We compare our best method, the combination of single image adaptation and image sequence adaptation (IA+SA), against state-of-the-art methods on standard datasets: Berkeley and COCO, introduced in Sec. 4.1, and the following:
Datasets. (1) GrabCut , 49 images with segmentation masks. (2) DAVIS16 , 50 high-resolution videos out of which we sample 10% of the frames uniformly at random as in [31, 28, 36, 52] and (3) PASCAL VOC12 validation, with 1449 images.
Results. Tab. 4 shows results. Our adaptation method (IA+SA) improves over the initial model on all datasets, and outperforms or matches state-of-the-art on 4 of the 5 (PASCAL VOC12, GrabCut, DAVIS16 and Berkeley). It brings improvements even when the initial model already offers strong performance and needs less than 4 clicks on average (PASCAL VOC12, GrabCut). The (small) improvement on PASCAL VOC12 further shows that our method helps even when the training and testing distributions match exactly.
On DAVIS16 our method shows strong improvement over the initial model and we match the state-of-the-art method , which operates at a higher output resolution. We note that the standard evaluation protocol favors adaptive methods, as the same objects appear repeatedly in the test sequence.
On COCO all adaptation methods substantially improve over the initial model, especially on the unseen classes, where adaptation is particularly useful. Interestingly, the impact of the adaptation is greater on COCO than on the other datasets. COCO is more challenging due to its many small objects and high appearance diversity. While some existing works report even better clicks@q% than us, these cannot be directly compared, since the 10 test objects on which previous works report results are not known. In addition, some previous works  predict at the full input image resolution, whereas our backbone network predicts at 1/4 of it. If we ignore objects smaller than pixels as in , our IA+SA method improves from 9.9 to 5.6 clicks@85% on the unseen classes of COCO. Importantly, our adaptation methods could be applied on top of any network architecture.
4.5 Ablation Study
We ablate the benefit of treating corrections as training examples (on COCO unseen 6k). For this, we selectively remove them from the loss (Eq. (4)). For single image adaptation, this leads to a parameter update that makes the model more confident in its current prediction, but this does not improve the segmentation masks. Instead, training on corrections improves clicks@85% from 13.2 to 10.6. For image sequence adaptation, switching off the corrections corresponds to treating the predicted mask as ground-truth and updating the model with it. This approach implicitly contains corrections in the mask and thus improves clicks@85% from 13.2 for the fixed initial model to 11.9. Explicitly using correction in the loss offers an additional gain of almost 2 clicks, down to 10. This shows that treating user corrections as training examples is key to our method: They are necessary for single image adaptation and highly beneficial for image sequence adaptation.
4.6 Implementation Details
, pre-trained on ImageNet and PASCAL VOC12 , at resolution . Batch size is , atrous rates
. Encoder output stride isand decoder stride is . We use SGD with momentum and initial learning rate with polynomial decay. We sample at most foreground and background corrections for stage one of training (Sec. 3.3). Corrections are encoded with disks of radius 3. (2) Adaptation: We adapt using Adam  with learning rate of and batch size . For single image adaptation we do 10 SGD steps and regularize with and . For image sequence adaptation we do a 1 SGD step and use and . For the DRIONS-DB dataset we use a larger learning rate of
. We repeated experiments 10 times to test variance and found it to be minimal (standard deviation). Hence, we only report averages to improve readability.
We proposed an interactive object segmentation method that treats user corrections as training examples to update the model on-the-fly to the data at hand. We have shown experimentally that this enables successfully adapting to distributions shifts and even large domain changes. We have evaluated our method on 8 diverse datasets. On all datasets it shows reductions in the number of click corrections compared to a fixed model.
Acknowledgement. We thank Rodrigo Benenson, Jordi Pont-Tuset and Thomas Mensink for their inputs on this work.
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