Scene Parsing With Caption-Based Weak Supervision
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Solution Overview
Problem
Existing scene parsing methods rely heavily on manually-annotated training datasets, making it difficult to scale up and generalize to complex scenes efficiently.
Innovation Solution
Utilize a trained image segmentation model to partition images into patches, generate patch embeddings using a vision transformer model, and employ a patch-label similarity model to generate word embeddings, followed by a label prediction model to produce text labels without relying on extensive manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually-annotated training datasets are used for scene parsing, then segmentation and relation prediction accuracy can be achieved, but the labor-intensive annotation process significantly limits scalability and efficiency
Solution Approach 1:
The system uses weakly-supervised learning where the model learns from image captions and automatically generates pseudo-labels for training, eliminating the need for extensive manual pixel-level annotations. The caption-based supervision signal enables the model to self-train on large-scale datasets without labor-intensive human annotation.
Solution Approach 2:
Image captions serve as an intermediary between the raw image data and the scene parsing task. Instead of requiring direct manual annotation of object boundaries and relationships, the system uses natural language captions as a intermediate representation that can be automatically generated, bridging the gap between unannotated images and structured scene understanding.
2Adaptability or versatility
If existing scene parsing methods are used, then basic scene understanding can be achieved, but generalizability to complex real-world scenes is significantly limited
Solution Approach 1:
The system segments the image into patches and processes them through a vision transformer architecture, dividing the complex scene understanding task into manageable local regions that are then aggregated to form global scene understanding. This hierarchical segmentation enables better generalization to complex scenes while managing computational complexity.
Solution Approach 2:
The system changes the representation parameters by using learned embedding spaces for both visual patches and textual captions. By transforming images into patch embeddings and captions into word embeddings, then computing similarities in this transformed parameter space, the model achieves better adaptability to diverse scenes while maintaining manageable computational complexity.
Data Source
AI summary
An embodiment partitions, using a trained image segmentation model, an input image into a plurality of patches. An embodiment generates, using a vision transformer model, a plurality of patch embeddings, each patch embedding comprising a multidimensional numerical representation of a patch in the plurality of patches. An embodiment generates, using a trained patch-label similarity model, a plurality of word embeddings corresponding to the plurality of patch embeddings. An embodiment generating, using a trained label prediction model and the plurality of word embeddings, a text label corresponding to the input image.