  Amodal Instance Segmentation (AIS) aims to segment the region of both visible
and possible occluded parts of an object instance. While Mask R-CNN-based AIS
approaches have shown promising results, they are unable to model high-level
features coherence due to the limited receptive field. The most recent
transformer-based models show impressive performance on vision tasks, even
better than Convolution Neural Networks (CNN). In this work, we present
AISFormer, an AIS framework, with a Transformer-based mask head. AISFormer
explicitly models the complex coherence between occluder, visible, amodal, and
invisible masks within an object's regions of interest by treating them as
learnable queries. Specifically, AISFormer contains four modules: (i) feature
encoding: extract ROI and learn both short-range and long-range visual
features. (ii) mask transformer decoding: generate the occluder, visible, and
amodal mask query embeddings by a transformer decoder (iii) invisible mask
embedding: model the coherence between the amodal and visible masks, and (iv)
mask predicting: estimate output masks including occluder, visible, amodal and
invisible. We conduct extensive experiments and ablation studies on three
challenging benchmarks i.e. KINS, D2SA, and COCOA-cls to evaluate the
effectiveness of AISFormer. The code is available at:
https://github.com/UARK-AICV/AISFormer
