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Found 7 papers, 5 papers with code
Path-Adaptive Matting for Efficient Inference Under Various Computational Cost Constraints
In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation
This adjustment enhances the structure for multi-head attention computation, leading to enhanced network performance and CBLA is a plug-and-play module.
End-to-End Human Instance Matting
Finally, an instance matting network decodes the image features and united semantics guidance to predict all instance-level alpha mattes.
Dual-Context Aggregation for Universal Image Matting
However, existing matting methods are designed for specific objects or guidance, neglecting the common requirement of aggregating global and local contexts in image matting.
Ranked #3 on
Image Matting
on Distinctions-646
Revisiting Context Aggregation for Image Matting
Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data.
Ranked #1 on
Image Matting
on Composition-1K
Long-Range Feature Propagating for Natural Image Matting
Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte.
Ranked #6 on
Image Matting
on Composition-1K
(using extra training data)
