TransMatting: Enhancing Transparent Objects Matting with Transformers
This addresses a specific challenge in computer vision for applications like image editing, but it is incremental as it builds on existing matting methods with a new architecture.
The paper tackles the problem of image matting for transparent objects, which lack known foreground areas, by proposing TransMatting, a Transformer-based network that achieves state-of-the-art results on matting benchmarks.
Image matting refers to predicting the alpha values of unknown foreground areas from natural images. Prior methods have focused on propagating alpha values from known to unknown regions. However, not all natural images have a specifically known foreground. Images of transparent objects, like glass, smoke, web, etc., have less or no known foreground. In this paper, we propose a Transformer-based network, TransMatting, to model transparent objects with a big receptive field. Specifically, we redesign the trimap as three learnable tri-tokens for introducing advanced semantic features into the self-attention mechanism. A small convolutional network is proposed to utilize the global feature and non-background mask to guide the multi-scale feature propagation from encoder to decoder for maintaining the contexture of transparent objects. In addition, we create a high-resolution matting dataset of transparent objects with small known foreground areas. Experiments on several matting benchmarks demonstrate the superiority of our proposed method over the current state-of-the-art methods.