Enhancing few-shot object detection through pseudo-label mining
Few-shot object detection involves adapting an existing detector to a set of unseen categories with few annotated examples. This data limitation makes these methods to underperform those trained on large labeled datasets. In many scenarios, there is a high amount of unlabeled data that is never expl...
| Authors: | ; ; |
|---|---|
| Format: | Article |
| Publication date: | 2025 |
| Country: | España |
| Institution: | Universidad de Santiago de Compostela (USC) |
| Repository: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Language: | English |
| OAI Identifier: | oai:minerva.usc.gal:10347/39982 |
| Online Access: | https://hdl.handle.net/10347/39982 |
| Access Level: | Open access |
| Keyword: | Few-shot Object detection Few-shot learning Pseudo-label mining Pseudo-labeling 120304 Inteligencia artificial |
| _version_ | 1878732795171307520 |
|---|---|
| author | García Fernández, Pablo |
| author2 | Cores Costa, Daniel Mucientes Molina, Manuel |
| author2_role | author author |
| author_browse | Cores Costa, Daniel García Fernández, Pablo Mucientes Molina, Manuel |
| author_facet | García Fernández, Pablo Cores Costa, Daniel Mucientes Molina, Manuel |
| author_role | author |
| collection | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| description | Few-shot object detection involves adapting an existing detector to a set of unseen categories with few annotated examples. This data limitation makes these methods to underperform those trained on large labeled datasets. In many scenarios, there is a high amount of unlabeled data that is never exploited. Thus, we propose to exPAND the initial novel set by mining pseudo-labels. From a raw set of detections, xPAND obtains reliable pseudo-labels suitable for training any detector. To this end, we propose two new modules: Class and Box confirmation. Class Confirmation aims to remove misclassified pseudo-labels by comparing candidates with expected class prototypes. Box Confirmation estimates IoU to discard inadequately framed objects. Experimental results demonstrate that xPAND enhances the performance of multiple detectors up to +5.9 nAP and +16.4 nAP50 points for MS-COCO and PASCAL VOC, respectively, establishing a new state of the art. Code: https://github.com/PAGF188/xPAND |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_49f1fc36b28d45fddba8a2a5cb81ee13 |
| institution | Universidad de Santiago de Compostela (USC) |
| instname_str | Universidad de Santiago de Compostela (USC) |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:minerva.usc.gal:10347/39982 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| spelling | Enhancing few-shot object detection through pseudo-label mining García Fernández, Pablo Cores Costa, Daniel Mucientes Molina, Manuel Few-shot Object detection Few-shot learning Pseudo-label mining Pseudo-labeling 120304 Inteligencia artificial Few-shot object detection involves adapting an existing detector to a set of unseen categories with few annotated examples. This data limitation makes these methods to underperform those trained on large labeled datasets. In many scenarios, there is a high amount of unlabeled data that is never exploited. Thus, we propose to exPAND the initial novel set by mining pseudo-labels. From a raw set of detections, xPAND obtains reliable pseudo-labels suitable for training any detector. To this end, we propose two new modules: Class and Box confirmation. Class Confirmation aims to remove misclassified pseudo-labels by comparing candidates with expected class prototypes. Box Confirmation estimates IoU to discard inadequately framed objects. Experimental results demonstrate that xPAND enhances the performance of multiple detectors up to +5.9 nAP and +16.4 nAP50 points for MS-COCO and PASCAL VOC, respectively, establishing a new state of the art. Code: https://github.com/PAGF188/xPAND Elsevier https://hdl.handle.net/10347/39982 |
| spellingShingle | Enhancing few-shot object detection through pseudo-label mining García Fernández, Pablo Few-shot Object detection Few-shot learning Pseudo-label mining Pseudo-labeling 120304 Inteligencia artificial |
| title | Enhancing few-shot object detection through pseudo-label mining |
| title_full | Enhancing few-shot object detection through pseudo-label mining |
| title_fullStr | Enhancing few-shot object detection through pseudo-label mining |
| title_full_unstemmed | Enhancing few-shot object detection through pseudo-label mining |
| title_short | Enhancing few-shot object detection through pseudo-label mining |
| title_sort | Enhancing few-shot object detection through pseudo-label mining |
| topic | Few-shot Object detection Few-shot learning Pseudo-label mining Pseudo-labeling 120304 Inteligencia artificial |
| topic_facet | Few-shot Object detection Few-shot learning Pseudo-label mining Pseudo-labeling 120304 Inteligencia artificial |
| url | https://hdl.handle.net/10347/39982 |