Cascaded Decoder Network for Portrait Segmentation and Matting
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Solution Overview
Problem
Existing image matting methods that do not require external guidance struggle with generalization on complex real images due to a lack of semantic guidance, leading to unsatisfactory global segmentation results.
Innovation Solution
The proposed method involves a neural network training approach that generates encoded features of different resolutions using an encoder, decodes these features using cascaded decoding modules, and predicts an output image using a head module, all while optimizing for both segmentation and matting tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If matting models capture semantics and details by end-to-end trainings on large-scale datasets without external guidance, then the methods are challenged in generalization when tested on complicated real images
Solution Approach 1:
The decoder is segmented into multiple cascaded decoding modules, each responsible for different resolution levels. This segmentation allows the model to process semantic information at different scales, improving both generalization and segmentation accuracy by handling global and local features separately.
Solution Approach 2:
The patent introduces multi-resolution decoding by adding a dimension of resolution levels. The cascaded decoding modules operate at different resolutions, transforming the feature processing from a single-scale operation to a multi-scale operation, thereby capturing both semantic guidance and detailed information effectively.
2Measurement precision
If a neural network model is trained to achieve high accuracy in portrait segmentation and matting, then computation overhead increases
Solution Approach 1:
The network architecture is segmented into encoder and decoder components, with the decoder further divided into cascaded modules. This segmentation allows efficient resource allocation, where the encoder extracts features once and the cascaded decoding modules progressively refine them, reducing redundant computation while maintaining high accuracy.
Solution Approach 2:
The cascaded decoding modules perform partial decoding at each resolution level, progressively refining the segmentation result. Instead of performing all decoding operations at once, the model applies partial decoding actions at multiple stages, reducing the computational burden at each step while achieving high overall accuracy.
Data Source
AI summary
The present disclosure provides methods and apparatuses of image processing, network model training, and application, and storage medium. The image processing method comprises: an encoding step of generating, based on an input image and an encoder, a plurality of encoded features of different resolutions; a decoding step of decoding based on the plurality of encoded features and a decoder of a plurality of cascaded decoding modules for a decoded feature of a same resolution as that of the input image; and a prediction step of predicting, based on the decoded feature and a head module, an output image having a same resolution as that of the input image.