Abstract
The activation function plays a crucial role in model optimisation, yet the optimal choice remains unclear. For example, the Sigmoid activation is the de-facto activation in balanced classification tasks, however, in imbalanced classification, it proves inappropriate due to bias towards frequent classes. In this work, we delve deeper in this phenomenon by performing a comprehensive statistical analysis in the classification and intermediate layers of both balanced and imbalanced networks and we empirically show that aligning the activation function with the data distribution, enhances the performance in both balanced and imbalanced tasks. To this end, we propose the Adaptive Parametric Activation (APA) function, a novel and versatile activation function that unifies most common activation functions under a single formula. APA can be applied in both intermediate layers and attention layers, significantly outperforming the state-of-the-art on several imbalanced benchmarks such as ImageNet-LT, iNaturalist2018, Places-LT, CIFAR100-LT and LVIS and balanced benchmarks such as ImageNet1K, COCO and V3DET. The code is available at https://github.com/kostas1515/AGLU.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alexandridis, K.P., Deng, J., Nguyen, A., Luo, S.: Long-tailed instance segmentation using gumbel optimized loss. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part X. LNCS, vol. 13670, pp. 353–369. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20080-9_21
Alexandridis, K.P., Luo, S., Nguyen, A., Deng, J., Zafeiriou, S.: Inverse image frequency for long-tailed image recognition. IEEE Trans. Image Process. 32, 5721–5736 (2023)
Alshammari, S., Wang, Y.X., Ramanan, D., Kong, S.: Long-tailed recognition via weight balancing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6897–6907 (2022)
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Beyer, L., Zhai, X., Kolesnikov, A.: Better plain ViT baselines for ImageNet-1k. arXiv preprint arXiv:2205.01580 (2022)
Cai, J., Wang, Y., Hwang, J.N.: ACE: ally complementary experts for solving long-tailed recognition in one-shot. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 112–121 (2021)
Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems (2019)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chen, X., et al.: AREA: adaptive reweighting via effective area for long-tailed classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19277–19287 (2023)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)
Cui, J., Liu, S., Tian, Z., Zhong, Z., Jia, J.: ResLT: residual learning for long-tailed recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3695–3706 (2022). https://doi.org/10.1109/TPAMI.2022.3174892
Cui, J., Zhong, Z., Liu, S., Yu, B., Jia, J.: Parametric contrastive learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 715–724 (2021)
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268–9277 (2019)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Dosovitskiy, A., et al.: An image is worth \(16\times 16\) words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=YicbFdNTTy
Feng, C., Zhong, Y., Huang, W.: Exploring classification equilibrium in long-tailed object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3417–3426 (2021)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, pp. 315–323 (2011)
Gupta, A., Dollar, P., Girshick, R.: LVIS: a dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019)
Han, B.: Wrapped cauchy distributed angular softmax for long-tailed visual recognition. arXiv preprint arXiv:2305.18732 (2023)
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, Y.Y., Wu, J., Wei, X.S.: Distilling virtual examples for long-tailed recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 235–244 (2021)
He, Y.Y., Zhang, P., Wei, X.S., Zhang, X., Sun, J.: Relieving long-tailed instance segmentation via pairwise class balance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7000–7009 (2022)
Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
Hong, Y., Zhang, J., Sun, Z., Yan, K.: SAFA: sample-adaptive feature augmentation for long-tailed image classification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXIV. LNCS, vol. 13684, pp. 587–603. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20053-3_34
Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., Chang, B.: Disentangling label distribution for long-tailed visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6626–6636 (2021)
Hsieh, T.I., Robb, E., Chen, H.T., Huang, J.B.: DropLoss for long-tail instance segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1549–1557 (2021)
Hsu, Y.C., Hong, C.Y., Lee, M.S., Geiger, D., Liu, T.L.: ABC-norm regularization for fine-grained and long-tailed image classification. IEEE Trans. Image Process. 32, 3885–3896 (2023)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5375–5384 (2016)
Hyun Cho, J., Krähenbühl, P.: Long-tail detection with effective class-margins. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part VIII. LNCS, vol. 13668, pp. 698–714. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20074-8_40
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Iscen, A., Araujo, A., Gong, B., Schmid, C.: Class-balanced distillation for long-tailed visual recognition (2021)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2021)
Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: Eighth International Conference on Learning Representations (ICLR) (2020)
Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3573–3587 (2017)
Kim, B., Kim, J.: Adjusting decision boundary for class imbalanced learning. IEEE Access 8, 81674–81685 (2020)
Li, B., et al.: Equalized focal loss for dense long-tailed object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6990–6999 (2022)
Li, B., Han, Z., Li, H., Fu, H., Zhang, C.: Trustworthy long-tailed classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6970–6979 (2022)
Li, J., Tan, Z., Wan, J., Lei, Z., Guo, G.: Nested collaborative learning for long-tailed visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6949–6958 (2022)
Li, T., Wang, L., Wu, G.: Self supervision to distillation for long-tailed visual recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 630–639 (2021)
Li, T., et al.: Targeted supervised contrastive learning for long-tailed recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6918–6928 (2022)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
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
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2537–2546 (2019)
Ma, Y., Jiao, L., Liu, F., Yang, S., Liu, X., Li, L.: Curvature-balanced feature manifold learning for long-tailed classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15824–15835 (2023)
Mahajan, D., et al.: Exploring the limits of weakly supervised pretraining. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 181–196 (2018)
Massey, F.J., Jr.: The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46(253), 68–78 (1951)
Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=37nvvqkCo5
Misra, D.: Mish: a self regularized non-monotonic activation function. arXiv preprint arXiv:1908.08681 (2019)
Pan, T.Y., et al.: On model calibration for long-tailed object detection and instance segmentation. In: Advances in Neural Information Processing Systems, vol. 34 (2021)
Parisot, S., Esperança, P.M., McDonagh, S., Madarasz, T.J., Yang, Y., Li, Z.: Long-tail recognition via compositional knowledge transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6939–6948 (2022)
Park, S., Hong, Y., Heo, B., Yun, S., Choi, J.Y.: The majority can help the minority: context-rich minority oversampling for long-tailed classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6887–6896 (2022)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)
Ren, J., et al.: Balanced meta-softmax for long-tailed visual recognition. In: Proceedings of Neural Information Processing Systems (NeurIPS) (2020)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Richards, F.J.: A flexible growth function for empirical use. J. Exp. Bot. 10(2), 290–301 (1959)
Samuel, D., Chechik, G.: Distributional robustness loss for long-tail learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9495–9504 (2021)
Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 467–482. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_29
Skorski, M., Temperoni, A., Theobald, M.: Revisiting weight initialization of deep neural networks. In: Asian Conference on Machine Learning, pp. 1192–1207. PMLR (2021)
Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., Beyer, L.: How to train your ViT? Data, augmentation, and regularization in vision transformers. arXiv preprint arXiv:2106.10270 (2021)
Tan, J., Lu, X., Zhang, G., Yin, C., Li, Q.: Equalization Loss v2: a new gradient balance approach for long-tailed object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1685–1694 (2021)
Tan, J., et al.: Equalization loss for long-tailed object recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11662–11671 (2020)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357. PMLR (2021)
Touvron, H., Cord, M., Jégou, H.: DeiT III: revenge of the ViT. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXIV. LNCS, vol. 13684, pp. 516–533. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20053-3_30
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Jégou, H.: Going deeper with image transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 32–42 (2021)
Van Horn, G., et al.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8769–8778 (2018)
Vigneswaran, R., Law, M.T., Balasubramanian, V.N., Tapaswi, M.: Feature generation for long-tail classification. In: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 1–9 (2021)
Wang, H., Fu, S., He, X., Fang, H., Liu, Z., Hu, H.: Towards calibrated hyper-sphere representation via distribution overlap coefficient for long-tailed learning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXIV. LNCS, vol. 13684, pp. 179–196. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20053-3_11
Wang, J., Lukasiewicz, T., Hu, X., Cai, J., Xu, Z.: RSG: a simple but effective module for learning imbalanced datasets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3784–3793 (2021)
Wang, J., et al.: V3Det: vast vocabulary visual detection dataset. arXiv preprint arXiv:2304.03752 (2023)
Wang, J., et al.: Seesaw loss for long-tailed instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9695–9704 (2021)
Wang, P., Han, K., Wei, X.S., Zhang, L., Wang, L.: Contrastive learning based hybrid networks for long-tailed image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 943–952 (2021)
Wang, T., et al.: The devil is in classification: a simple framework for long-tail instance segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 728–744. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_43
Wang, T., et al.: C2AM Loss: chasing a better decision boundary for long-tail object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6980–6989 (2022)
Wang, T., Zhu, Y., Zhao, C., Zeng, W., Wang, J., Tang, M.: Adaptive class suppression loss for long-tail object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3103–3112 (2021)
Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.: Long-tailed recognition by routing diverse distribution-aware experts. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=D9I3drBz4UC
Wang, Y.X., Ramanan, D., Hebert, M.: Learning to model the tail. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Woo, S., et al.: ConvNeXt V2: co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16133–16142 (2023)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Xu, Z., Chai, Z., Yuan, C.: Towards calibrated model for long-tailed visual recognition from prior perspective. In: Advances in Neural Information Processing Systems, vol. 34, pp. 7139–7152 (2021)
Zang, Y., Huang, C., Loy, C.C.: FASA: feature augmentation and sampling adaptation for long-tailed instance segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3457–3466 (2021)
Zhang, S., Chen, C., Peng, S.: Reconciling object-level and global-level objectives for long-tail detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 18982–18992 (2023)
Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2361–2370 (2021)
Zhang, Y., Wei, X.S., Zhou, B., Wu, J.: Bag of tricks for long-tailed visual recognition with deep convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 4, pp. 3447–3455 (2021). https://doi.org/10.1609/aaai.v35i4.16458
Zhao, Y., Chen, W., Tan, X., Huang, K., Zhu, J.: Adaptive logit adjustment loss for long-tailed visual recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 3472–3480 (2022)
Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16489–16498 (2021)
Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9719–9728 (2020)
Zhou, Y., Qu, Y., Xu, X., Shen, H.: ImbSAM: a closer look at sharpness-aware minimization in class-imbalanced recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11345–11355 (2023)
Zhou, Z., Li, L., Zhao, P., Heng, P.A., Gong, W.: Class-conditional sharpness-aware minimization for deep long-tailed recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3499–3509 (2023)
Zhu, J., Wang, Z., Chen, J., Chen, Y.P.P., Jiang, Y.G.: Balanced contrastive learning for long-tailed visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6908–6917 (2022)
Zhu, L., Yang, Y.: Inflated episodic memory with region self-attention for long-tailed visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4344–4353 (2020)
Zou, Y., Yu, Z., Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 289–305 (2018)
Acknowledgements
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) project “ViTac: Visual-Tactile Synergy for Handling Flexible Materials” (EP/T033517/2).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Alexandridis, K.P., Deng, J., Nguyen, A., Luo, S. (2025). Adaptive Parametric Activation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15112. Springer, Cham. https://doi.org/10.1007/978-3-031-72949-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-72949-2_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72948-5
Online ISBN: 978-3-031-72949-2
eBook Packages: Computer ScienceComputer Science (R0)