Weakly-Supervised Object Detection Through Pseudo-Label Refinement
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Solution Overview
Problem
Creating labeled training data for machine learning models is a long and complicated process, especially for object detection tasks, which requires significant effort and resources.
Innovation Solution
A weakly-supervised object detection framework that utilizes image-level labels without spatial information, employing a multi-branch network with an image branch and refinement branches to generate pseudo-labels and refine object detection using dropout techniques and sequential batch back-propagation to manage memory consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised object detection with detailed bounding box annotations is used, then detection accuracy is improved, but the complexity and time required for data preparation increases significantly
Solution Approach 1:
The system performs preliminary object detection and generates pseudo-labels with bounding boxes automatically before fine-tuning. This preliminary action creates initial training data that guides subsequent refinement, reducing the need for manual annotation while maintaining detection accuracy through a two-stage process where coarse predictions are refined iteratively
Solution Approach 2:
The system uses its own detection outputs to generate training labels through pseudo-labeling. The detector automatically creates bounding box annotations for training data, enabling self-supervised learning where the model generates its own training signals without requiring external manual labeling, thereby eliminating the time-consuming data preparation process
2Measurement precision
If detailed bounding box annotations are required for training, then object detection precision is improved, but the ease of data collection deteriorates
Solution Approach 1:
The system automatically generates pseudo-labels with bounding boxes from image-level annotations alone. The model processes easily collected image-level data and self-generates the detailed bounding box annotations needed for precise detection, eliminating the need for manual box drawing while maintaining high detection precision through iterative refinement
Solution Approach 2:
The system handles multiple types of annotations (image-level tags and bounding boxes) through a unified framework. It can work with either image-level annotations for initial training or incorporate bounding boxes for fine-tuning, making the data collection process flexible and adaptable to different annotation availability scenarios
3Reliability
If full supervised training with extensive labeled data is used, then model reliability is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The system uses only image-level annotations for initial training rather than requiring complete bounding box annotations for all training images. This partial supervision approach provides enough signal for reliable learning while significantly reducing annotation complexity and computational overhead, with optional fine-tuning on a smaller subset of boxed data
Data Source
AI summary
Apparatuses, systems, and techniques to detect object in images including digital representations of those objects. In at least one embodiment, one or more objects are detected in an image based, at least in part, on one or more pseudo-labels corresponding to said one or more objects.