IEICE TRANSACTIONS on Information
Two-Path Object Knowledge Injection for Detecting Novel Objects With Single-Stage Dense Detector
Summary :
We present an effective system for integrating generative zero-shot classification modules into a YOLO-like dense detector to detect novel objects. Most double-stage-based novel object detection methods are achieved by refining the classification output branch but cannot be applied to a dense detector. Our system utilizes two paths to inject knowledge of novel objects into a dense detector. One involves injecting the class confidence for novel classes from a classifier trained on data synthesized via a dual-step generator. This generator learns a mapping function between two feature spaces, resulting in better classification performance. The second path involves re-training the detector head with feature maps synthesized on different intensity levels. This approach significantly increases the predicted objectness for novel objects, which is a major challenge for a dense detector. We also introduce a stop-and-reload mechanism during re-training for optimizing across head layers to better learn synthesized features. Our method relaxes the constraint on the detector head architecture in the previous method and has markedly enhanced performance on the MSCOCO dataset.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.11 pp.1868-1880
- Publication Date
- 2023/11/01
- Publicized
- 2023/08/02
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7216
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
KuanChao CHU
University of Tokyo
Hideki NAKAYAMA
University of Tokyo
Keyword
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KuanChao CHU, Hideki NAKAYAMA, "Two-Path Object Knowledge Injection for Detecting Novel Objects With Single-Stage Dense Detector" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 11, pp. 1868-1880, November 2023, doi: 10.1587/transinf.2022EDP7216.
Abstract: We present an effective system for integrating generative zero-shot classification modules into a YOLO-like dense detector to detect novel objects. Most double-stage-based novel object detection methods are achieved by refining the classification output branch but cannot be applied to a dense detector. Our system utilizes two paths to inject knowledge of novel objects into a dense detector. One involves injecting the class confidence for novel classes from a classifier trained on data synthesized via a dual-step generator. This generator learns a mapping function between two feature spaces, resulting in better classification performance. The second path involves re-training the detector head with feature maps synthesized on different intensity levels. This approach significantly increases the predicted objectness for novel objects, which is a major challenge for a dense detector. We also introduce a stop-and-reload mechanism during re-training for optimizing across head layers to better learn synthesized features. Our method relaxes the constraint on the detector head architecture in the previous method and has markedly enhanced performance on the MSCOCO dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7216/_p
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@ARTICLE{e106-d_11_1868,
author={KuanChao CHU, Hideki NAKAYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Two-Path Object Knowledge Injection for Detecting Novel Objects With Single-Stage Dense Detector},
year={2023},
volume={E106-D},
number={11},
pages={1868-1880},
abstract={We present an effective system for integrating generative zero-shot classification modules into a YOLO-like dense detector to detect novel objects. Most double-stage-based novel object detection methods are achieved by refining the classification output branch but cannot be applied to a dense detector. Our system utilizes two paths to inject knowledge of novel objects into a dense detector. One involves injecting the class confidence for novel classes from a classifier trained on data synthesized via a dual-step generator. This generator learns a mapping function between two feature spaces, resulting in better classification performance. The second path involves re-training the detector head with feature maps synthesized on different intensity levels. This approach significantly increases the predicted objectness for novel objects, which is a major challenge for a dense detector. We also introduce a stop-and-reload mechanism during re-training for optimizing across head layers to better learn synthesized features. Our method relaxes the constraint on the detector head architecture in the previous method and has markedly enhanced performance on the MSCOCO dataset.},
keywords={},
doi={10.1587/transinf.2022EDP7216},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Two-Path Object Knowledge Injection for Detecting Novel Objects With Single-Stage Dense Detector
T2 - IEICE TRANSACTIONS on Information
SP - 1868
EP - 1880
AU - KuanChao CHU
AU - Hideki NAKAYAMA
PY - 2023
DO - 10.1587/transinf.2022EDP7216
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2023
AB - We present an effective system for integrating generative zero-shot classification modules into a YOLO-like dense detector to detect novel objects. Most double-stage-based novel object detection methods are achieved by refining the classification output branch but cannot be applied to a dense detector. Our system utilizes two paths to inject knowledge of novel objects into a dense detector. One involves injecting the class confidence for novel classes from a classifier trained on data synthesized via a dual-step generator. This generator learns a mapping function between two feature spaces, resulting in better classification performance. The second path involves re-training the detector head with feature maps synthesized on different intensity levels. This approach significantly increases the predicted objectness for novel objects, which is a major challenge for a dense detector. We also introduce a stop-and-reload mechanism during re-training for optimizing across head layers to better learn synthesized features. Our method relaxes the constraint on the detector head architecture in the previous method and has markedly enhanced performance on the MSCOCO dataset.
ER -
English
