LSTD
LSTD performs low-shot transfer detection within the Progressive Object Transfer Detection (POTD) framework by distilling object knowledge from a well-annotated source detector to improve a target detector's object detection performance using limited annotations.
Key Features:
- Low-Shot Learning: Utilizes a small number of annotated examples to improve detection performance in target domains by transferring knowledge from a source detector.
- Transfer Knowledge (TK) Module: Transfers learned features from a well-trained source model to a less-annotated target model to enhance detection capabilities.
- Background Depression (BD) Module: Suppresses background responses to increase emphasis on relevant object regions within images.
- Integration with SSD (Single Shot MultiBox Detector): Incorporates SSD as the region proposal network (RPN) to generate candidate object bounding boxes.
- Weakly-Supervised Transfer Detection (WSTD) and Recurrent Object Labeling: Employs recurrent object labeling to handle weakly-labeled images and further refine the target detector using supervision from LSTD.
- Progressive Object Transfer Detection (POTD) framework: Uses a staged approach that leverages prior knowledge and minimal annotations for progressive detector adaptation.
Scientific Applications:
- Medical imaging: Adapts object detectors to new medical imaging targets with few annotated examples.
- Wildlife monitoring: Detects novel species or objects in ecological imagery where annotations are scarce.
- Rapid adaptation to new objects or conditions: Enables deployment of detectors in domains that require quick generalization from limited labeled data.
Methodology:
Two-stage transfer process: (1) Low-Shot Transfer Detection (LSTD) distills knowledge from a source detector with abundant annotations to enhance a target detector with few annotations; (2) Weakly-Supervised Transfer Detection (WSTD) applies a recurrent object labeling mechanism to weakly-labeled images, exploiting supervision from LSTD to further train the target detector.
Topics
Details
- Tool Type:
- command-line tool
- Added:
- 11/14/2019
- Last Updated:
- 12/22/2020
Operations
Publications
Chen H, Wang Y, Wang G, Bai X, Qiao Y. Progressive Object Transfer Detection. IEEE Transactions on Image Processing. 2020;29:986-1000. doi:10.1109/tip.2019.2938680. PMID:31502974.