A Survey of Zero-Shot Object Detection
Zero-Shot object Detection (ZSD), one of the most challenging problems in the field of object detection, aims to accurately identify new categories that are not encountered during training. Recent advancements in deep learning and increased computational power have led to significant improvements in...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2025-05-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849304127973097472 |
|---|---|
| author | Weipeng Cao Xuyang Yao Zhiwu Xu Ye Liu Yinghui Pan Zhong Ming |
| author_facet | Weipeng Cao Xuyang Yao Zhiwu Xu Ye Liu Yinghui Pan Zhong Ming |
| author_sort | Weipeng Cao |
| collection | DOAJ |
| description | Zero-Shot object Detection (ZSD), one of the most challenging problems in the field of object detection, aims to accurately identify new categories that are not encountered during training. Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems, achieving high recognition accuracy on benchmark datasets. However, these systems remain limited in real-world applications due to the scarcity of labeled training samples, making it difficult to detect unseen classes. To address this, researchers have explored various approaches, yielding promising progress. This article provides a comprehensive review of the current state of ZSD, distinguishing four related methods—zero-shot, open-vocabulary, open-set, and open-world approaches—based on task objectives and data usage. We highlight representative methods, discuss the technical challenges within each framework, and summarize the commonly used evaluation metrics, benchmark datasets, and experimental results. Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD. |
| format | Article |
| id | doaj-art-6dd9dd48a638454e957955d34d2550c3 |
| institution | Kabale University |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-6dd9dd48a638454e957955d34d2550c32025-08-20T03:55:49ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-05-018372675010.26599/BDMA.2024.9020098A Survey of Zero-Shot Object DetectionWeipeng Cao0Xuyang Yao1Zhiwu Xu2Ye Liu3Yinghui Pan4Zhong Ming5Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen), Shenzhen 518107, China, and also with the National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaNational Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaZero-Shot object Detection (ZSD), one of the most challenging problems in the field of object detection, aims to accurately identify new categories that are not encountered during training. Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems, achieving high recognition accuracy on benchmark datasets. However, these systems remain limited in real-world applications due to the scarcity of labeled training samples, making it difficult to detect unseen classes. To address this, researchers have explored various approaches, yielding promising progress. This article provides a comprehensive review of the current state of ZSD, distinguishing four related methods—zero-shot, open-vocabulary, open-set, and open-world approaches—based on task objectives and data usage. We highlight representative methods, discuss the technical challenges within each framework, and summarize the commonly used evaluation metrics, benchmark datasets, and experimental results. Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.https://www.sciopen.com/article/10.26599/BDMA.2024.9020098zero-shot object detection (zsd)open-vocabulary object detectionopen-set object detectionopen-world object detection |
| spellingShingle | Weipeng Cao Xuyang Yao Zhiwu Xu Ye Liu Yinghui Pan Zhong Ming A Survey of Zero-Shot Object Detection Big Data Mining and Analytics zero-shot object detection (zsd) open-vocabulary object detection open-set object detection open-world object detection |
| title | A Survey of Zero-Shot Object Detection |
| title_full | A Survey of Zero-Shot Object Detection |
| title_fullStr | A Survey of Zero-Shot Object Detection |
| title_full_unstemmed | A Survey of Zero-Shot Object Detection |
| title_short | A Survey of Zero-Shot Object Detection |
| title_sort | survey of zero shot object detection |
| topic | zero-shot object detection (zsd) open-vocabulary object detection open-set object detection open-world object detection |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020098 |
| work_keys_str_mv | AT weipengcao asurveyofzeroshotobjectdetection AT xuyangyao asurveyofzeroshotobjectdetection AT zhiwuxu asurveyofzeroshotobjectdetection AT yeliu asurveyofzeroshotobjectdetection AT yinghuipan asurveyofzeroshotobjectdetection AT zhongming asurveyofzeroshotobjectdetection AT weipengcao surveyofzeroshotobjectdetection AT xuyangyao surveyofzeroshotobjectdetection AT zhiwuxu surveyofzeroshotobjectdetection AT yeliu surveyofzeroshotobjectdetection AT yinghuipan surveyofzeroshotobjectdetection AT zhongming surveyofzeroshotobjectdetection |