Open-Vocabulary Image Segmentation With Iterative Mask Refinement
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Solution Overview
Problem
Existing image segmentation methods are inflexible and struggle to handle unexpected objects that do not fit into a predefined class catalogue, particularly in automated driving applications, necessitating a more adaptable and accurate 'open-vocabulary' segmentation.
Innovation Solution
A method using a vision processing network that outputs pixel masks and a weight matrix, refined through an encoder network, allowing for iterative refinement and alignment with a text encoder network to adapt to unseen classes, enhancing segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed catalogue of classes is used for segmentation, then the segmentation process is simple and fast, but it cannot handle unexpected objects that do not fit into the predefined classes
Solution Approach 1:
The patent applies dynamics by making the segmentation system adaptable to new object classes dynamically. Instead of using a fixed class catalogue, the system uses learnable query embeddings that can be updated and refined iteratively to recognize unexpected objects. The vision processing network continuously learns from new data, allowing the segmentation to adapt to novel object types without requiring complete retraining or manual class definition updates.
Solution Approach 2:
The patent changes the parameter representation from fixed class labels to continuous vector embeddings. The query embeddings are initialized and then refined through iterative processing, allowing the system to adapt its parameters dynamically. This enables the segmentation to handle open-vocabulary scenarios where object classes are not predetermined, as the embeddings can represent both known and unknown object types flexibly.
2Adaptability or versatility
If open-vocabulary segmentation is implemented to handle unexpected objects, then adaptability improves, but segmentation accuracy may deteriorate due to lack of predefined class constraints
Solution Approach 1:
The patent implements feedback through iterative refinement of query embeddings. The vision processing network processes the input image multiple times, each iteration refining the embeddings based on the previous results. This feedback loop allows the system to progressively improve segmentation accuracy for both known and unknown object classes, ensuring that open-vocabulary capability does not compromise precision.
Solution Approach 2:
The patent applies preliminary action by pre-initializing query embeddings before the iterative refinement process. These initial embeddings provide a starting point that incorporates prior knowledge, and then the system refines them through multiple processing iterations. This preliminary setup ensures that even unexpected objects benefit from structured initial representations, maintaining accuracy while achieving open-vocabulary segmentation.
3Measurement precision
If multiple iterations of vision processing are performed to refine masks and weight matrices, then segmentation accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by performing a fixed number of refinement iterations rather than continuing until convergence. This approach achieves sufficient segmentation accuracy for practical applications while avoiding excessive processing time. The system performs just enough iterations to capture the essential features needed for accurate segmentation of both known and unknown objects, balancing precision and efficiency.
Data Source
AI summary
A method for determining a segmentation of an input image. The segmentation assigns, to each pixel of the input image, a class of an entity that has given rise to the pixel value of the pixel. The method includes: providing the input image to a vision processing network that outputs masks designating sets of pixels belonging to different object types, and an associated weight matrix that is indicative of distinguishing features characterizing entities of different types in the input image; transforming, by an encoder network, the weight matrix in combination with the input image into at least one mask representation in a latent space that is a notion of assignments of classes to masks; processing the input image, together with the mask representation, by a vision processing network, into a refinement for the masks and a refined weight matrix.