Amodal object completion is a complex task that involves predicting the
invisible parts of an object based on visible segments and background
information. Learning shape priors is crucial for effective amodal completion,
but traditional methods often rely on two-stage processes or additional
information, leading to inefficiencies and potential error accumulation. To
address these shortcomings, we introduce a novel framework named the
Hyper-Transformer Amodal Network (H-TAN). This framework utilizes a hyper
transformer equipped with a dynamic convolution head to directly learn shape
priors and accurately predict amodal masks. Specifically, H-TAN uses a
dual-branch structure to extract multi-scale features from both images and
masks. The multi-scale features from the image branch guide the hyper
transformer in learning shape priors and in generating the weights for dynamic
convolution tailored to each instance. The dynamic convolution head then uses
the features from the mask branch to predict precise amodal masks. We
extensively evaluate our model on three benchmark datasets: KINS, COCOA-cls,
and D2SA, where H-TAN demonstrated superior performance compared to existing
methods. Additional experiments validate the effectiveness and stability of the
novel hyper transformer in our framework.

使用 Hyper-Transformer Amodal Network (H-TAN) 框架，通过学习形状先验并准确预测不完整物体的掩模，解决了传统方法中依赖两个阶段流程或额外信息的效率问题和潜在错误累积问题，该框架使用双分支结构从图像和掩模提取多尺度特征，通过动态卷积生成每个实例的权重来指导超级变换器学习形状先验，然后使用掩模分支中的特征预测精确的不完整物体掩模，在三个基准数据集上进行了广泛评估并证明了 H-TAN 相对于现有方法的优越性能，额外实验证实了该新型超级变换器在框架中的有效性和稳定性。