# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Method** & **Major Innovations and Contributions** \\ \hline CAMatting [14] & Proposed a two-encoder-two-decoder network to estimate the alpha matte and foreground image simultaneously. \\ \hline HRMatting [26] & An approach specifically designed for high-resolution images, which involves running matting by cropping and stitching image patches. This approach can effectively process high-resolution images. \\ \hline LFPNet [27] & Proposed a two-stream structure to learn long-range features outside the receptive field that can help distinguishing local foreground and background. \\ \hline DAPM [8] & This is the pioneering end-to-end matting algorithm that can perform the task without the need for a trimap. \\ \hline SHM [10] & Proposed the first two-stage CNN matting model, which uses a semantic segmentation model to generate a trimap and inputs it into the matting model to generate the alpha matte. \\ \hline JMNet [22] & Introduction of a posture estimation module to provide global structural guidance and local focus of attention. \\ \hline SalientIM [31] & Combined a salient object detection model to achieve a matting algorithm for the arbitrary salient foreground. \\ \hline LFM [18] & Employ two decoder branches to make predictions on the foreground and background of an image, which are subsequently fed into the fusion module to generate the alpha matte. \\ \hline GFM [36] & Designed a network consisting of a shared encoder and two decoders, which serve to learn semantic features and extract low-level structures to preserve details. \\ \hline PP-Matting [35] & Similar to the idea of GFM [36], a guidance module is designed on this basis, which uses semantic features to provide guidance for detail prediction. \\ \hline P3M [30] & The first algorithm designed for privacy-preserving portrait matting. \\ \hline AlphaNet [20] & Developed an attention module that directs the up-sampling and down-sampling processes of the matting network, refining the alpha matte edges. \\ \hline HATMatting [19] & Combining appearance cues and pyramidal features through the integration of both spatial and channel attention mechanisms; uses hybrid loss functions to guide the network to improve foreground structure. \\ \hline AIM [29] & Used attention to generate specific trimaps for different foregrounds, guiding the matting network to improve the details in unknown regions. \\ \hline BGM [21] & Proposed a matting algorithm that uses a background image as an auxiliary input to generate the alpha matte instead of a trimap. \\ \hline BGMV2 [28] & Implemented a real-time video matting algorithm using the background as an auxiliary input; Runs at 60 fps in HD on modern GPUs. \\ \hline ModNet [33] & Proposed a CNN-based efficient and real-time human video matting method that does not require a trimap. \\ \hline RVM [34] & Introduced ConvGRU module to utilize temporal information and achieved a robust real-time video matting algorithm. \\ \hline AM [11] & Proposed a novel active learning approach that utilizes reinforcement learning to automatically determine the most informative labeling regions, while also obtaining a satisfactory alpha matte in user interaction. \\ \hline SmartScribbles [23] & The model is able to effectively identify informative regions in the image and use information propagation methods to reduce the user scribbles. It can generate a high-quality alpha matte based on limited user scribbles. \\ \hline UGIM [38] & An end-to-end CNN is designed in this method that supports arbitrary forms of trimap inputs, from completely unknown trimaps to carefully edited ones. The foreground and background regions of the trimmed map can be guided by user scribbles. \\ \hline IIM [32] & This method generates heatmaps as prior information based on the user’s clicks. The model predicts the foreground and background based on the click location, uses uncertainty estimation for fine-tuning adjustment, and then generates the alpha matte. \\ \hline DIIM [37] & The model converts clicks into Euclidean distance maps and inputs them along with the origin image to the CNN for predicting the alpha matte. \\ \hline \hline \end{tabular}
\end{table}