Vision-Language Pseudo Labeling for Unlabeled Object Detection
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Solution Overview
Problem
Training accurate visual object detectors requires large amounts of annotated data, which is costly and labor-intensive, especially when handling a large number of categories.
Innovation Solution
Utilizing vision and language models to generate pseudo labels from unlabeled images by encoding image and text vectors, leveraging the rich semantics in these models to localize and categorize objects, thereby reducing the need for human annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of annotated data are used to train accurate visual object detectors, then detection accuracy is improved, but annotation cost and labor requirements increase significantly
Solution Approach 1:
The system uses the object detector itself to generate pseudo-labels for unlabeled images, creating a self-service annotation mechanism. The detector processes unlabeled images, generates predictions with confidence scores, and automatically creates training annotations without human intervention, thereby reducing annotation costs while maintaining detection accuracy through iterative self-improvement
Solution Approach 2:
The system performs preliminary annotation by generating pseudo-labels for unlabeled images before actual training occurs. By pre-processing unlabeled data into annotated format using the vision-language model and region scorer, the system prepares training data in advance, reducing the need for costly manual annotation during the training phase
2Adaptability or versatility
If the number of categories the detector should handle becomes large, then detection coverage is improved, but annotation effort becomes even harder
Solution Approach 1:
The system introduces a vision-language model as an intermediary that bridges the gap between unlabeled images and category annotations. This intermediary model, combined with the region scorer, automatically generates category predictions for diverse objects in unlabeled images, eliminating the need for manual annotation across numerous categories while maintaining broad detection coverage
Solution Approach 2:
The system creates copies of annotation patterns from the vision-language model's predictions across multiple categories. By generating pseudo-labels that replicate the structure and quality of manual annotations for diverse categories, the system enables scalable category coverage without proportionally increasing annotation effort
Data Source
AI summary
A method for object detection obtains, from a set of RGB images lacking annotations, a set of regions that include potential objects, a bounding box, and an objectness score indicating a region prediction confidence. The method obtains, by a region scorer for each region in the set, a category from a fixed set of categories and a confidence for the category responsive to the objectness score. The method duplicates each region in the set to obtain a first and a second patch. The method encodes the patches to obtain an image vector. The method encodes a template sentence using the category to obtain a text vector for each category. The method compares the image vector to the text vector via a similarity function to obtain a similarity probability based on the confidence. The method defines a final set of pseudo labels based on the similarity probability being above a threshold.