Transformer-Based Object Detection via Pseudo-Labeling
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Solution Overview
Problem
Current object detection systems rely heavily on manually annotated training data, which is time-consuming, expensive, and limited to the classes annotated during training, making it difficult to detect novel objects without extensive labeled data, especially in domains like product compliance where dynamic legal requirements necessitate efficient detection of logos and pictograms.
Innovation Solution
The proposed system, SPOT (Surfacing Positions using Transformers), employs a learnable Robust Feature Mapping module and transformer-based architectures to automatically generate labels and annotations for object detection, enabling detection of novel classes with minimal training data by emphasizing visual cues from query images and performing robust feature matching, even when exposed to unseen classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually annotated training data is used, then detection accuracy for known classes is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system uses a pre-trained model to generate pseudo-labels by copying detection patterns from known classes to novel classes. The pre-trained model's learned features are transferred to automatically annotate new object classes without manual intervention, resolving the contradiction between accuracy and annotation time
Solution Approach 2:
The system performs preliminary training on known classes first, then uses this pre-trained knowledge to automatically handle novel classes. The pre-training phase prepares feature extractors and detectors that can be quickly adapted to new classes through automatic pseudo-labeling, reducing subsequent annotation time while maintaining accuracy
2Reliability
If manually annotated training data is used, then model training effectiveness is improved, but cost increases significantly
Solution Approach 1:
The system copies detection capabilities from pre-trained models to automatically generate training labels for novel classes. This eliminates the need for expensive manual annotation while preserving model training effectiveness through transfer learning and automatic pseudo-label generation
Solution Approach 2:
The system performs self-annotation by using its own pre-trained knowledge to automatically label novel classes. The model serves itself by generating pseudo-labels without external human annotators, reducing cost while maintaining training effectiveness
3Measurement precision
If extensive labeled data is used, then detection capability for specific classes is improved, but adaptability to novel classes deteriorates
Solution Approach 1:
The system achieves multi-functionality by enabling the same detection model to handle both known and novel classes. Through pre-training on diverse data and automatic pseudo-labeling, the model becomes universal, adapting to new object classes without extensive retraining or manual annotation
Solution Approach 2:
The system changes the training paradigm from extensive class-specific labeled data to automatic pseudo-label generation. By modifying how training data is obtained (from manual annotation to automated pseudo-labeling), the model gains adaptability to novel classes while maintaining detection capability
4Measurement precision
If traditional object detection systems are used, then detection precision for known objects is maintained, but efficiency in detecting novel objects deteriorates
Solution Approach 1:
The system performs preliminary pre-training on diverse object classes to build robust feature extractors. This preliminary action enables efficient detection of novel classes by transferring learned features, maintaining precision while improving productivity for unseen objects
Solution Approach 2:
The system copies detection patterns and features from pre-trained models to automatically detect novel classes. This copying mechanism preserves detection precision for known objects while enabling efficient detection of novel objects through transferred knowledge
Data Source
AI summary
Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.