The pascal visual object classes (VOC) challenge
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Multimodal fusion combines sources such as vision, language, depth, LiDAR, and speech to enhance scene semantic understanding.
“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]
Section 5.1 describes fusion of multiple visual representations for image classification, and Section 6.1.1 says high-performing methods combined visual feature channels. The paper does not support the claimed combination of vision with language, depth, LiDAR, and speech for scene semantic understanding. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredYOLOv1 was benchmarked on VOC Pascal Dataset 2007 and 2012 and used the Darknet framework for training.
“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]
The Introduction identifies VOC2007 as an annotated dataset, and the Abstract describes VOC as an object-detection benchmark. The citation is attached to the dataset description, but this paper does not establish that YOLOv1 was benchmarked on VOC2007 or VOC2012. The Darknet training detail falls outside the cited phrase and is not addressed by this source. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMost object detection models use general-purpose datasets where vehicle images are usually taken from a lateral or frontal view instead of a cenital plane.
“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]
The Introduction identifies VOC as a publicly available object-detection dataset that includes cars. Section 3.1 describes varied viewing conditions, and the Additional Information section says high-confidence car detections show varying poses; neither establishes that VOC car images are usually lateral or frontal rather than overhead. The paper also does not establish how widely object-detection models use such datasets. (LLM, conf=0.86)
Experimental — accuracy not yet measuredRPNs with Fast R-CNNs produce detection accuracy better than the strong baseline of Selective Search with Fast R-CNNs on PASCAL VOC detection benchmarks.
“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]
For FSCIOD tasks, the data partitioning strategy by Perez-Rua et al. utilizes 20 classes overlapping with PASCAL VOC as new incremental classes and the remaining 60 as base data, with models evaluated using K ∈ {1,5,10} bounding boxes per new class.
“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]
PASCAL VOC is widely used for object detection tasks and includes 20 common object classes like people, animals, vehicles, and household items.
“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]
The one-stage object detection approach has better execution exactness and speed on MS COCO and PASCAL VOC datasets.
“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]
Section 5.2 describes methods entered in the VOC detection challenge, and Section 6.2.1 compares their detection results. Our copy does not report that a one-stage approach has better accuracy and speed on both MS COCO and PASCAL VOC; searches for those terms and execution speed found no supporting passage. Page numbers were not available in the indexed passages. (LLM, conf=0.75)
Experimental — accuracy not yet measuredFor evaluation of generic object detection algorithms, bounding boxes are most widely used in the current literature.
“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 practice, current detectors focus mainly on structured object categories, such as the 20, 200 and 91 object classes in PASCAL VOC, ILSVRC and MS COCO respectively.
“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
PASCAL VOC is a multiyear effort devoted to creation and maintenance of benchmark datasets for classification and object detection, creating precedent for standardized evaluation via annual competitions.
“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
For generic object detection, there are four famous datasets: PASCAL VOC, ImageNet, MS COCO, and Open Images.
“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
Current detection datasets contain only a few dozen to hundreds of categories, significantly fewer than those which can be recognized by humans.
“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
Semantic labeling of pixels in a variety of scene types reaches performance levels of around 80% accuracy or more.
“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 popular detection frameworks, regions without bounding-box annotations are treated as background during training.
“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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“We evaluate these models on PASCAL VOC 2007 object detection [5].”
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- 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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