PASCAL VOC

The PASCAL Visual Object Classes Challenge: A Retrospective. The PASCAL Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available dataset of images together with ground truth annotation and standardised evaluation software; and (ii) an annual competition and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008–2012. The paper is intended for two audiences: algorithm designers, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and, challenge designers, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community’s progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision, 2012) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.


References in zbMATH (referenced in 125 articles )

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  1. Cauchois, Maxime; Gupta, Suyash; Duchi, John C.: Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction (2021)
  2. Ma, Fan; Meng, Deyu; Dong, Xuanyi; Yang, Yi: Self-paced multi-view co-training (2020)
  3. Sun, Xiao; Lian, Zhouhui: EasyMesh: an efficient method to reconstruct 3D mesh from a single image (2020)
  4. Daneshmand, Amir; Sun, Ying; Scutari, Gesualdo; Facchinei, Francisco; Sadler, Brian M.: Decentralized dictionary learning over time-varying digraphs (2019)
  5. Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) arXiv
  6. Joy, Thomas; Desmaison, Alban; Ajanthan, Thalaiyasingam; Bunel, Rudy; Salzmann, Mathieu; Kohli, Pushmeet; Torr, Philip H. S.; Kumar, M. Pawan: Efficient relaxations for dense CRFs with sparse higher-order potentials (2019)
  7. Koteswara Rao, L.; Rohini, P.; Pratap Reddy, L.: Local color oppugnant quantized extrema patterns for image retrieval (2019)
  8. Naiel, Mohamed A.; Ahmad, M. Omair; Swamy, M. N. S.: A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains (2019)
  9. Rajan, Purnima; Ma, Yongming; Jedynak, Bruno: Cox processes for counting by detection (2019)
  10. Rao, Cong; Fan, Yi; Su, Kaile; Latecki, Longin Jan: Common object discovery as local search for maximum weight cliques in a global object similarity graph (2019)
  11. Shafieezadeh-Abadeh, Soroosh; Kuhn, Daniel; Esfahani, Peyman Mohajerin: Regularization via mass transportation (2019)
  12. Zhang, Dingwen; Han, Junwei; Zhao, Long; Meng, Deyu: Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework (2019)
  13. Ahmad, Shahzor; Cheong, Loong-Fah: Robust detection and affine rectification of planar homogeneous texture for scene understanding (2018)
  14. Ghanta, Sindhu; Dy, Jennifer G.; Niu, Donglin; Jordan, Michael I.: Latent marked Poisson process with applications to object segmentation (2018)
  15. Larsson, Måns; Arnab, Anurag; Zheng, Shuai; Torr, Philip; Kahl, Fredrik: Revisiting deep structured models for pixel-level labeling with gradient-based inference (2018)
  16. Qin, Yao; Feng, Mengyang; Lu, Huchuan; Cottrell, Garrison W.: Hierarchical cellular automata for visual saliency (2018)
  17. Raviv, Dolev; Hazan, Tamir; Osadchy, Margarita: Hinge-minimax learner for the ensemble of hyperplanes (2018)
  18. Song, Zhiguo; Sun, Jifeng; Yu, Jialin; Liu, Shengqing: Robust visual tracking via patch descriptor and structural local sparse representation (2018)
  19. Zhang, Suofei; Xing, Lingzhi; Zhou, Lin; Sun, Zhixin: Object tracking by incremental structural learning of deformable parts (2018)
  20. Zhang, Yuanjian; Miao, Duoqian; Zhang, Zhifei; Xu, Jianfeng; Luo, Sheng: A three-way selective ensemble model for multi-label classification (2018)

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