ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation
CoRR(2024)
摘要
Amodal Instance Segmentation (AIS) presents a challenging task as it involves
predicting both visible and occluded parts of objects within images. Existing
AIS methods rely on a bidirectional approach, encompassing both the transition
from amodal features to visible features (amodal-to-visible) and from visible
features to amodal features (visible-to-amodal). Our observation shows that the
utilization of amodal features through the amodal-to-visible can confuse the
visible features due to the extra information of occluded/hidden segments not
presented in visible display. Consequently, this compromised quality of visible
features during the subsequent visible-to-amodal transition. To tackle this
issue, we introduce ShapeFormer, a decoupled Transformer-based model with a
visible-to-amodal transition. It facilitates the explicit relationship between
output segmentations and avoids the need for amodal-to-visible transitions.
ShapeFormer comprises three key modules: (i) Visible-Occluding Mask Head for
predicting visible segmentation with occlusion awareness, (ii) Shape-Prior
Amodal Mask Head for predicting amodal and occluded masks, and (iii)
Category-Specific Shape Prior Retriever aims to provide shape prior knowledge.
Comprehensive experiments and extensive ablation studies across various AIS
benchmarks demonstrate the effectiveness of our ShapeFormer. The code is
available at: https://github.com/UARK-AICV/ShapeFormer
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