Example-Based Object Detection Method Reduces Retraining Costs
A new approach to object detection leverages false positive and false negative samples to correct persistent detection errors without full model retraining. Traditional open-vocabulary models like SAM3 can detect arbitrary objects from prompts and sometimes outperform category-specific detectors, but repeated misdetections of the same object remain problematic in engineering applications. The proposed method uses existing error samples to prevent future mistakes, cutting down on human effort, computational resources, and time. The research is published on arXiv under ID 2605.04501.
Key facts
- Object detection has advanced with open-vocabulary approaches that detect arbitrary objects from human prompts.
- Models like SAM3 can outperform category-specific detectors without additional training on specific datasets.
- False positives and false negatives still occur in object detection.
- Persistent misdetections of the same object are unacceptable in practical engineering.
- Retraining models for each error is costly in human effort, computation, and time.
- The new method uses existing false positive and false negative samples to prevent errors.
- The paper is available on arXiv with ID 2605.04501.
Entities
Institutions
- arXiv