SOTAVerified

Image Matting

Image Matting is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.

Source: Automatic Trimap Generation for Image Matting

Image Source: Real-Time High-Resolution Background Matting

Papers

Showing 176–200 of 225 papers

TitleStatusHype
Scalable Matting: A Sub-linear Approach—0
SGM-Net: Semantic Guided Matting Net—0
SHDM-NET: Heat Map Detail Guidance with Image Matting for Industrial Weld Semantic Segmentation Network—0
Simultaneous Video Defogging and Stereo Reconstruction—0
Situational Perception Guided Image Matting—0
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting—0
Smart Scribbles for Image Mating—0
SOLO: A Simple Framework for Instance Segmentation—0
Sparse Coding for Alpha Matting—0
Towards Enhancing Fine-grained Details for Image Matting—0
Training-Free Neural Matte Extraction for Visual Effects—0
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation—0
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans—0
Video Editing with Temporal, Spatial and Appearance Consistency—0
Video Magnification in Presence of Large Motions—0
Video Matting via Consistency-Regularized Graph Neural Networks—0
Video Matting via Sparse and Low-Rank Representation—0
Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting—0
Visual Object Tracking: The Initialisation ProblemCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Sub-frame Appearance and 6D Pose Estimation of Fast Moving ObjectsCode0
Intra-frame Object Tracking by DeblattingCode0
TOM-Net: Learning Transparent Object Matting from a Single ImageCode0
AlphaGAN: Generative adversarial networks for natural image mattingCode0
Fourier-Domain Optimization for Image ProcessingCode0
Show:102550
← PrevPage 8 of 9Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DIMMSE14—Unverified
2IndexNet-MattingMSE13—Unverified
3Context-Aware MattingMSE8.2—Unverified
4SIMMSE5.8—Unverified
5LSAMattingMSE5.4—Unverified
6FBAMattingMSE5.3—Unverified
7PP-MattingMSE5—Unverified
8LFPNetMSE4.1—Unverified
9MatteFormerMSE4—Unverified
10TMFNetMSE3.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SHMCSAD61.5—Unverified
2LFSAD36.12—Unverified
3HATTSAD28.01—Unverified
4SHMSAD17.81—Unverified
5GFM(r)SAD10.89—Unverified
6GFM(d)SAD10.26—Unverified
7GFM(r2b)SAD10.24—Unverified
8GFM(r')SAD9.66—Unverified
9StyleMatteSAD9.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LFSAD42.95—Unverified
2HATTSAD25.99—Unverified
3SHMSAD21.56—Unverified
4GFMSAD13.2—Unverified
5P3M-Net (r)SAD8.73—Unverified
6StyleMatteSAD6.97—Unverified
7P3M-Net (v)SAD6.24—Unverified
#ModelMetricClaimedVerifiedStatus
1LFSAD191.74—Unverified
2SHMSAD170.44—Unverified
3U2NETSAD83.46—Unverified
4GFMSAD52.66—Unverified
5AIM-NetSAD43.92—Unverified
6DiffMatteSAD16.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CAMMSE4.5—Unverified
2BMMSE1.33—Unverified
3IMMSE1.16—Unverified
4Adobe LS-GANMSE0.97—Unverified
#ModelMetricClaimedVerifiedStatus
1PP-MattingSAD40.69—Unverified
2DCAMSAD31.27—Unverified
3ViTMatteSAD17.05—Unverified
4DiffMatteSAD15.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MODNet+MAD0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1MODNet+ (Our)MAD0.97—Unverified