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The Pascal Visual Object Classes (VOC) Challenge
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“It combines various sources of information, such as vision, language, depth data, lidar, and speech, to enhance the capability of scene semantic understanding [35,36,37].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The architecture, benchmarked on the VOC Pascal Dataset 2007 and 2012 [29], exploited the Darknet framework for training.”
From YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Most object detection models use generalpurpose datasets, such as COCO [12], ImageNet [13], and VOC [14]), where vehicle (i.e., cars) images are usually taken from a lateral or frontal view instead of a cenital plane.”
From T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks (2023) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“We comprehensively evaluate our method on the PASCAL VOC detection benchmarks [11] where RPNs with Fast R-CNNs produce detection accuracy better than the strong baseline of Selective Search with Fast R-CNNs.”
From Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2017) · cites this paper as [11]
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“For FSCIOD tasks, the data partitioning strategy by PerezRua et al. [62] is commonly used. This approach utilizes 20 classes overlapping with the PASCAL VOC dataset [63] as new incremental classes and the remaining 60 as base data. FSCIOD models under this setup are evaluated using K ∈ 1 , 5 , 10 bounding boxes per new class.”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [63]
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“The PASCAL Visual Object Classes (VOC) dataset, widely used for object detection tasks, includes 20 common object classes like people, animals, vehicles, and household items [63].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [63]
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In the citing paper
“On the other hand, one one-stage approach has better ongoing execution exactness and speed on MS COCO and PASCAL VOC datasets [24,25].”
From Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System (2023) · cites this paper as [24]
Experimental — accuracy not yet measuredIn the citing paper
“To the best of our knowledge, for the evaluation of generic object detection algorithms, it is bounding boxes which are most widely used in the current literature (Everingham et al. 2010;Russakovsky et al. 2015), and therefore this is also the approach we adopt in this survey.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Everingham et al. 2010
In the citing paper
“In practice, current detectors focus mainly on structured object categories, such as the 20, 200 and 91 object classes in PASCAL VOC (Everingham et al. 2010), ILSVRC (Russakovsky et al. 2015) and MS COCO (Lin et al. 2014) respectively.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Everingham et al. 2010
In the citing paper
“PASCAL VOC Everingham et al. (2010, 2015) is a multiyear effort devoted to the creation and maintenance of a series of benchmark datasets for classification and object detection, creating the precedent for standardized evaluation of recognition algorithms in the form of annual competitions.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Everingham et al. 2010
In the citing paper
“For generic object detection, there are four famous datasets: PASCAL VOC (Everingham et al. 2010(Everingham et al. , 2015)), ImageNet (Deng et al. 2009), MS COCO (Lin et al. 2014) and Open Images (Kuznetsova et al. 2018).”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Everingham et al. 2010
In the citing paper
“Current detection datasets (Everingham et al. 2010;Russakovsky et al. 2015;Lin et al. 2014) contain only a few dozen to hundreds of categories, significantly fewer than those which can be recognized by humans.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Everingham et al. 2010
In the citing paper
“Likewise semantic labeling of pixels in a variety of scene types reaches performance levels of around 80% accuracy or more [74].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [74]
In the citing paper
“regions without bounding box annotations are simply considered as background during training in currently popular detection frameworks [18], [19]”
From IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection (2021) · cites this paper as [18]
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Metadata
- Authors
- Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A.
- Affiliations
- University of Leeds (GB)
- University of Edinburgh (GB)
- Microsoft Research (United Kingdom) (GB)
- University of Oxford (GB)
- Corresponding author
- Everingham, Mark
- Venue
- International Journal of Computer Vision
- Published
- 2009-09-09
- Volume
- 88
- Issue
- 2
- Pages
- 303-338
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2031489346
- Cited by
- 19,599
- References
- 60
- Citations by year
- 2026: 1543 · 2025: 2006 · 2024: 2022 · 2023: 2109 · 2022: 2071 · 2021: 2130 · 2020: 1755 · 2019: 1514 · 2018: 1003 · 2017: 804 · 2016: 641 · 2015: 602 · 2014: 506 · 2013: 352 · 2012: 233
- FWCI
- 180.39
- Type
- journal article
- Language
- en
- Open access
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Vision (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- object detection, benchmark dataset, image annotation, evaluation procedures, object classification
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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