We propose a novel and effective method for high-quality instance segmentation. Top-performing "detect-then-segment" approaches (e.g., Mask R-CNN) rely on region-of-interest (ROI) cropping operations to obtain the final masks, but their performance is restricted by blurry boundary and average loss weight. Here, we develop a unique perspective of image segmentation as an image matting problem. Our method, termed MMask R-CNN, enjoys two advantages: 1) Present a novel matting enhanced mask head to generate trimap-based mat-ting features as auxiliary priors, which enhance accurate estimation of alpha values for boundary pixels. 2) Explicitly represents the classification uncertainty with confidence indicator alpha matte, and design a Uncertainty-Aware Binary Cross-Entropy Loss to assign larger weights to pixels with higher uncertainty. We evaluate the proposed method through extensive experiments on the COCO dataset. The experimental results show that our method outperforms well-tuned Mask R-CNN baseline by 2.3% AP.

We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolutions to refine instance features before the final prediction. In this paper, we first give an empirical comparison of different refinement strategies, which reveals that the widely-used four consecutive convolutions are not necessary. As an alternative, weight-sharing convolution blocks provides competitive performance. When such block is iterated for infinite times, the block output will eventually converge to an equilibrium state. Based on this observation, the implicit feature refinement (IFR) is developed by constructing an implicit function. The equilibrium state of instance features can be obtained by fixed-point iteration via a simulated infinite-depth network. Our IFR enjoys several advantages: 1) simulates an infinite-depth refinement network while only requiring parameters of single residual block; 2) produces high-level equilibrium instance features of global receptive field; 3) serves as a plug-and-play general module easily extended to most object recognition frameworks. Experiments on the COCO and YouTube-VIS benchmarks show that our IFR achieves improved performance on state-of-the-art image/video instance segmentation frameworks, while reducing the parameter burden (e.g. 1% AP improvement on Mask R-CNN with only 30.0% parameters in mask head). Code will be made available at \hrefhttps://github.com/lufanma/IFR.git https://github.com/lufanma/IFR.git .

In this paper, we propose an end-to-end framework for instance segmentation. Based on the recently introduced DETR, our method, termed SOLQ, segments objects by learning unified queries. In SOLQ, each query represents one object and has multiple representations: class, location and mask. The object queries learned perform classification, box regression and mask encoding simultaneously in an unified vector form. During training phase, the mask vectors encoded are supervised by the compression coding of raw spatial masks. In inference time,mask vectors produced can be directly transformed to spatial masks by the inverse process of compression coding. Experimental results show that SOLQ can achieve state-of-the-art performance, surpassing most of existing approaches. Moreover, the joint learning of unified query representation can greatly improve the detection performance of DETR. We hope our SOLQ can serve as a strong baseline for the Transformer-based instance segmentation.