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Articles


RESEARCH REVIEW: MASK TRANSFINER FOR HIGH-QUALITY INSTANCE SEGMENTATION

Mask Transfiner improves on state-of-the-art segmentation techniques by
capturing the fine details of objects.
Becca Miller
May 10, 2022



Recent advances in computer vision have led to the development of increasingly
powerful, highly accurate object detection techniques. When applied to image
segmentation, however, the performance of these approaches remains inadequate.
Standard image segmentation techniques can produce a coarse mask over an object,
but they don’t always capture the object’s finer details. Masks frequently
extend past the boundaries of an object or miss higher-frequency details.

As described in the paper “Mask Transfiner for High-Quality Instance
Segmentation,” researchers from ETH Zürich, HKUST, and Kuaishou Technology
developed Mask Transfiner, a technique for performing high-quality image
segmentation. The paper discusses the architecture of this model and evaluates
it against existing state-of-the-art segmentation techniques. In both
quantitative and qualitative analysis, Mask Transfiner consistently outperformed
standard segmentation techniques.


HOW MASK TRANSFINER WORKS

The most difficult object regions to segment tend to be those that contain
high-resolution features, such as the headlights on a car or the narrow branches
of a tree. Mask Transfiner detects these difficult-to-segment sections, then
performs a deeper analysis to refine those regions. By applying this fine-tuning
approach only to error-prone features, Mask Transfiner can greatly improve final
mask quality with little increase in computation or memory requirements.


IDENTIFYING INCOHERENT REGIONS

First, Mask Transfiner identifies incoherent or error-prone regions. When
segmenting an image, high-resolution information is often lost when an object
mask is applied to the image. This introduces error to the segmentation, since
object details of a smaller resolution than the object mask cannot be captured.
This is demonstrated in Figure 1.

Figure 1. Visualization of incoherent regions based on a simulation of
information loss during mask application. Source: “Mask Transfiner for
High-Quality Instance Segmentation”; used with permission

The incoherent regions, where high-resolution information has been lost, need to
be analyzed with more granularity to create higher-quality segmentations. In
general, these regions typically fall around the boundaries of the object or in
areas containing high-frequency details.

To identify these regions, Mask Transfiner’s incoherent-region detector takes as
its input the initial object mask as well as features of various scales. A
convolutional network then predicts which cells within the mask demonstrate the
greatest loss of information. These areas are then upsampled and fused with
high-resolution neighborhood features before being passed to another convolution
layer. This layer again detects which regions demonstrate the greatest loss of
information. These cells will be refined further throughout this process.


BUILD A QUADTREE FROM THE INCOHERENT REGIONS

Once the incoherent regions are detected, the researchers build a quadtree from
their points. The corner points of each cell form the quadtree’s root nodes, and
at each descending level of the tree, the algorithm subdivides the incoherent
regions by four. This technique breaks the incoherent regions into finer
subsections, so that Mask Transfiner can achieve the level of detail necessary
to achieve highly accurate segmentation.


REFINE THE MASK PREDICTIONS

After the researchers build the quadtree, Mask Transfiner refines the object
mask by performing a finer analysis on the incoherent regions. The refinement
network takes the quadtree as input and acts directly on its nodes, predicting
object probabilities for each incoherent region at increasingly smaller scales. 

This refinement network contains three primary components: a node encoder, a
sequence encoder, and a pixel decoder. The node encoder builds the feature
embedding for each incoherent point within the quadtree. The sequence encoder
takes these feature embeddings and processes them across multiple quadtree
levels, generating an output for each node. Finally, the pixel decoder decrypts
this output and predicts the final mask label for each pixel.

Because Mask Transfiner acts only on a small subset of image features—those
included in the quadtree—it’s possible to perform this high-resolution analysis
without large increases in computation or memory.


HOW MASK TRANSFINER PERFORMED AGAINST STATE-OF-THE-ART SOLUTIONS

After developing Mask Transfiner, the researchers compared its performance
against state-of-the-art techniques using benchmark datasets such as COCO,
Cityscapes, and BDD100K. On all three datasets, Mark Transfiner outperformed
standard techniques.

On the COCO dataset, for example, it outperformed RefineMask by 1.3 average
precision (AP), BCNet by 0.9 AP, and Query Inst by 1.7 AP. On the Cityscapes
dataset, the Mask Transfiner excelled in improving precision along object
boundaries, outperforming PointRend by 1.3 AP and BMask R-CNN by 2.3 AP along
object boundaries.

This improvement in precision was also visible when qualitatively comparing the
outputs of Mask Transfiner to the outputs of other techniques. These differences
were especially noticeable when examining difficult-to-segment object regions,
such as the giraffes’ legs in Figure 2. Other regions with large differences in
performance are marked with boxes:




Figure 2. Segmentation of images in the COCO validation set, using Mask-R-CNN,
SOLQ, PointRend, and the Mask Transfiner, respectively. Mask Transfiner provides
better results in highly detailed regions compared to other techniques. Regions
with large performance differences are marked with boxes. Source: “Mask
Transfiner for High-Quality Instance Segmentation”; used with permission.


WHAT TO TRY NEXT

Mask Transfiner improves on state-of-the-art image segmentation techniques by
providing high-quality object segmentation that can capture the fine details of
an object. Because of its highly efficient approach, Mask Transfiner produces
higher-quality results with very little increase in computational power. The
technique makes it possible to automatically detect the boundaries of objects
with little manual correction needed.

Try Mask Transfiner for yourself; the code and trained models from the study are
available here.


LEARN MORE

 * Read the full paper, “Mask Transfiner for High-Quality Instance Segmentation”


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