Summary of Self-supervised Cross-modality Learning For Uncertainty-aware Object Detection and Recognition in Applications Which Lack Pre-labelled Training Data, by Irum Mehboob et al.
Self-supervised cross-modality learning for uncertainty-aware object detection and recognition in applications which lack pre-labelled training data
by Irum Mehboob, Li Sun, Alireza Astegarpanah, Rustam Stolkin
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a self-supervising teacher-student pipeline to train deep neural networks for object detection, recognition, and localization in RGB images without annotated training datasets. The approach involves a simple teacher classifier trained on a few labeled 2D thumbnails that automatically processes unlabelled RGB-D data to teach a student network based on YOLOv3 architecture. The paper demonstrates the effectiveness of this method in detecting, recognizing, and localizing objects, including nuclear mixed-waste materials in cluttered scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how an AI model can learn to detect objects without needing lots of labeled training data. A simple teacher model is trained on a few labeled images, then uses this knowledge to teach a student model how to detect objects in many more unlabelled images. The paper also develops a way to estimate the uncertainty or confidence level of the model’s predictions. This approach can be used for important industrial tasks like sorting and handling nuclear waste. |
Keywords
» Artificial intelligence » Object detection » Student model » Teacher model
