Mask Guided Gated Convolution for Amodal Content Completion

TL;DR

This paper introduces a mask-guided gated convolutional model that improves the reconstruction of occluded objects by focusing on visible regions, leading to higher quality amodal content completion results.

Contribution

The novel use of weighted masks with gated convolutions enhances occluded object reconstruction, especially for uniform textures, surpassing baseline models.

Findings

Higher quality, texture-rich reconstructions

Effective focus on visible regions improves predictions

Self-supervised training on COCOA dataset

Abstract

We present a model to reconstruct partially visible objects. The model takes a mask as an input, which we call weighted mask. The mask is utilized by gated convolutions to assign more weight to the visible pixels of the occluded instance compared to the background, while ignoring the features of the invisible pixels. By drawing more attention from the visible region, our model can predict the invisible patch more effectively than the baseline models, especially in instances with uniform texture. The model is trained on COCOA dataset and two subsets of it in a self-supervised manner. The results demonstrate that our model generates higher quality and more texture-rich outputs compared to baseline models. Code is available at: https://github.com/KaziwaSaleh/mask-guided.