AdaptIS: Adaptive Instance Selection Network
Konstantin Sofiiuk, Olga Barinova, Anton Konushin

TL;DR
AdaptIS is a novel network architecture for class-agnostic instance segmentation that generates accurate masks for objects based on input points, adaptable to complex shapes and occlusions, and achieves state-of-the-art results on major benchmarks.
Contribution
It introduces an adaptive instance selection network using AdaIN layers for flexible, point-based object mask generation in segmentation tasks.
Findings
Achieves state-of-the-art results on Cityscapes and Mapillary datasets.
Performs competitively on the COCO dataset.
Effectively segments objects with complex shapes and occlusions.
Abstract
We present Adaptive Instance Selection network architecture for class-agnostic instance segmentation. Given an input image and a point , it generates a mask for the object located at . The network adapts to the input point with a help of AdaIN layers, thus producing different masks for different objects on the same image. AdaptIS generates pixel-accurate object masks, therefore it accurately segments objects of complex shape or severely occluded ones. AdaptIS can be easily combined with standard semantic segmentation pipeline to perform panoptic segmentation. To illustrate the idea, we perform experiments on a challenging toy problem with difficult occlusions. Then we extensively evaluate the method on panoptic segmentation benchmarks. We obtain state-of-the-art results on Cityscapes and Mapillary even without pretraining on COCO, and show competitive results on a…
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Taxonomy
MethodsAverage Pooling · ResNeXt Block · Grouped Convolution · Bottleneck Residual Block · Global Average Pooling · Residual Block · Residual Connection · *Communicated@Fast*How Do I Communicate to Expedia? · Kaiming Initialization · Max Pooling
