Pluralistic Object Detection Using Token-Guided Segmentation Masks
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Solution Overview
Problem
Existing salient object detection (SOD) technologies struggle with ambiguity in defining multiple salient objects due to annotation inconsistencies in datasets like DUTS, leading to suboptimal performance in generating single segmentation masks.
Innovation Solution
A pluralistic object detector is trained to generate multiple segmentation masks for multiple salient objects using a set of tokens, and a quality predictor assigns scores to these masks without requiring ground truth, enabling diverse segmentation scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single segmentation mask is generated for each image, then the system maintains simplicity in output structure, but it fails to resolve ambiguity when multiple salient objects are present
Solution Approach 1:
The patent segments the salient object detection task by introducing multiple learnable tokens, where each token corresponds to a different segmentation scenario. The object detector generates multiple segmentation masks (first segmentation mask, second segmentation mask, etc.) by processing the input image with different tokens, allowing the system to handle multiple salient objects without requiring a single complex model architecture.
Solution Approach 2:
The patent introduces tokens as intermediary elements that mediate between the input image and the segmentation masks. These tokens serve as placeholders that guide the object detector to generate specific segmentation masks corresponding to different salient object scenarios, resolving ambiguity without directly modifying the core detection architecture.
2Measurement precision
If multiple segmentation masks are generated using multiple tokens, then ambiguity in defining multiple salient objects is resolved, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements a quality predictor that automatically evaluates and scores multiple segmentation masks without requiring manual ground truth annotation. The quality predictor analyzes each generated mask and assigns a quality score, enabling the system to self-select the most appropriate segmentation mask based on intrinsic quality metrics rather than external validation, thus reducing time loss.
Solution Approach 2:
The system employs feedback mechanisms where the quality predictor continuously evaluates generated segmentation masks and provides quality scores back to the selection process. This feedback loop allows the system to iteratively refine mask selection based on quality metrics, improving precision while managing computational efficiency through intelligent feedback-driven selection.
3Adaptability or versatility
If annotation inconsistencies from multiple human annotators are used for training, then diverse perspectives are captured, but the supervision signal becomes ambiguous and adversely affects SOD performance
Solution Approach 1:
The patent segments the ambiguous annotation problem by associating different tokens with different annotator perspectives or segmentation scenarios. Each token learns to represent specific annotation patterns from the training data, allowing the model to disambiguate between different valid interpretations of salient objects rather than treating all annotations as a single ambiguous target.
Solution Approach 2:
The patent changes the parameter representation by introducing token embeddings that capture different annotation styles and perspectives. By transforming the supervision signal from a single ground truth mask to multiple token-associated masks, the system preserves the diversity of annotator perspectives while maintaining reliable supervision through structured parameter representation.
Data Source
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AI summary
Example solutions for pluralistic salient object detection are disclosed. A received image shows multiple objects, such as a first object and a second object. A pluralistic object detector is trained to learn tokens. When provided with the image and the first token, it generates a first segmentation mask corresponding to the first object, but not the second object, and when provided with the image and the second token, it generates a second segmentation mask corresponding to at least the second object (and possibly also the first image). When the pluralistic object detector is trained on five tokens, up to five different segmentation masks, each corresponding to a different selection of up to five objects, may be generated. Additionally, a quality predictor is disclosed that assigns quality scores to each of the different segmentation masks, without requiring ground truth for the image.