.png){kind=icon}
返回
Revisiting instance search: A new benchmark using cycle self-training
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.06.027.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Instance search aims at retrieving a particular object instance from a set of scene images. Although studied in previous competitions like TRECVID, there have been limited literature or datasets on this topic. In this paper, to overcome the generalization issue when arbitrary categories are involved in search and to benefit from the large amount of unlabeled data, we propose a cycle self-training framework which trains the instance search pipeline with automatic supervision. Given the two-stage pipeline with a localization and ranking module, the cycle self-training includes a ranker-guided localizer, and a localizer-guided ranker, each carefully designed to handle noisy labels that come with self-supervision. Furthermore, we build and release large-scale groundtruth annotations for instances to facilitate the algorithm evaluation and analysis in this research topic, especially for small objects in complex background. The datasets are publicly available at https://github.com/instance-search/instance-search. Extensive experiments show the effectiveness of the proposed cycle self-training framework and the superior performance compared with other state-of-the-art methods.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Instance search
Self training
Instance detection
Instance retrieval
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY随机样本共识-模型拟合的范例，可应用于图像分析和自动制图
OAAI
OAAI分享收藏分享收藏
查看更多内容