Abstract
Object detection requires substantial labeling effort for learning robust models. Active learning can reduce this effort by intelligently selecting relevant examples to be annotated. However, selecting these examples properly without introducing a sampling bias with a negative impact on the generalization performance is not straightforward and most active learning techniques can not hold their promises on real-world benchmarks. In our evaluation paper, we focus on active learning techniques without a computational overhead besides inference, something we refer to as zero-cost active learning. In particular, we show that a key ingredient is not only the score on a bounding box level but also the technique used for aggregating the scores for ranking images. We outline our experimental setup and also discuss practical considerations when using active learning for object detection.
Supported by Investitionsbank Berlin, Germany and computational resources of the BMBF grant programme “KI-Nachwuchs@FH”.
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References
Adhikari, B., Peltomäki, J., Germi, S.B., Rahtu, E., Huttunen, H.: Effect of label noise on robustness of deep neural network object detectors. In: Habli, I., Sujan, M., Gerasimou, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2021. LNCS, vol. 12853, pp. 239–250. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-83906-2_19
Agarwal, S., Arora, H., Anand, S., Arora, C.: Contextual diversity for active learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 137–153. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_9
Alex Kendall, V.B., Cipolla, R.: Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 57.1–57.12. BMVA Press (2017). https://doi.org/10.5244/C.31.57
Brust, C.A., Käding, C., Denzler, J.: Active learning for deep object detection. In: Computer Vision Theory and Applications (VISAPP), pp. 181–190 (2019). https://doi.org/10.5220/0007248601810190
Choi, J., Elezi, I., Lee, H.J., Farabet, C., Alvarez, J.M.: Active learning for deep object detection via probabilistic modeling. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10264–10273 (2021)
Citovsky, G., et al.: Batch active learning at scale. Adv. Neural. Inf. Process. Syst. 34, 11933–11944 (2021)
Everingham, M., Eslami, S., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98–136 (2015)
Feng, Z., et al.: ALBench: a framework for evaluating active learning in object detection. ar**v preprint ar**v:2207.13339 (2022)
Freeman, L.C.: Elementary Applied Statistics: For Students in Behavioral Science. Wiley, New York (1965)
Freytag, A., Rodner, E., Denzler, J.: Selecting influential examples: active learning with expected model output changes. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 562–577. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_37
Gal, Y.: Uncertainty in deep learning. Ph.D. thesis, University of Cambridge (2016)
Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2372–2379. IEEE (2009)
LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)
Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Machine Learning Proceedings 1994, pp. 148–156. Elsevier (1994)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 3–12. Springer, London (1994). https://doi.org/10.1007/978-1-4471-2099-5_1
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Reiß, S., Seibold, C., Freytag, A., Rodner, E., Stiefelhagen, R.: Every annotation counts: multi-label deep supervision for medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9532–9542 (2021)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28 (2015)
Rodner, E., Denzler, J.: One-shot learning of object categories using dependent Gaussian processes. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 232–241. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15986-2_24
Rodner, E., Hoffman, J., Donahue, J., Darrell, T., Saenko, K.: Towards adapting imagenet to reality: scalable domain adaptation with implicit low-rank transformations. ar**v preprint ar**v:1308.4200 (2013)
Roth, D., Small, K.: Margin-based active learning for structured output spaces. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 413–424. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842_40
Roy, S., Unmesh, A., Namboodiri, V.P.: Deep active learning for object detection. In: Proceedings of the British Machine Vision Conference (BMVC), p. 91 (2018)
Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: International Conference on Learning Representations (ICLR) (2017)
Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https://github.com/facebookresearch/detectron2
Yu, W., Zhu, S., Yang, T., Chen, C.: Consistency-based active learning for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3951–3960 (2022)
Yuan, T., et al.: Multiple instance active learning for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5330–5339 (2021)
Zhdanov, F.: Diverse mini-batch active learning. ar**v preprint ar**v:1901.05954 (2019)
Zheng, M., You, S., Huang, L., Wang, F., Qian, C., Xu, C.: SimMatch: semi-supervised learning with similarity matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14471–14481 (2022)
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Probst, D., Raza, H., Rodner, E. (2023). Evaluating Zero-Cost Active Learning for Object Detection. In: Masci, P., Bernardeschi, C., Graziani, P., Koddenbrock, M., Palmieri, M. (eds) Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops. SEFM 2022. Lecture Notes in Computer Science, vol 13765. Springer, Cham. https://doi.org/10.1007/978-3-031-26236-4_4
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