Mask Guided Gated Convolution for Amodal Content Completion
This work addresses amodal content completion for computer vision applications, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of reconstructing partially visible objects by using a weighted mask with gated convolutions to focus on visible pixels, resulting in higher quality and more texture-rich outputs compared to baseline models.
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.