Abstract
Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged information from vision-language models (VLMs) trained on large text-images datasets for the task. These methods learn an independent classifier for each object (class), overlooking correlations in their occurrences. Such co-occurrences can be captured from the training data as conditional probabilities between a pair of classes. We propose a framework to extend the independent classifiers by incorporating the co-occurrence information for object pairs to improve the performance of independent classifiers. We use a Graph Convolutional Network (GCN) to enforce the conditional probabilities between classes, by refining the initial estimates derived from image and text sources obtained using VLMs. We validate our method on four MLR datasets, where our approach outperforms all state-of-the-art methods.
S. Rawlekar and S. Bhatnagar—Equal contribution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdelfattah, R., Guo, Q., Li, X., Wang, X., Wang, S.: Cdul: Clip-driven unsupervised learning for multi-label image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1348–1357 (2023)
Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inform. 18(4), 1261–1271 (2014)
Bhatnagar, S., Ahuja, N.: Piecewise-linear manifolds for deep metric learning. In: Conference on Parsimony and Learning. pp. 269–281. PMLR (2024)
Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems 32 (2019)
Chang, W.C., Jiang, D., Yu, H.F., Teo, C.H., Zhang, J., Zhong, K., Kolluri, K., Hu, Q., Shandilya, N., Ievgrafov, V., et al.: Extreme multi-label learning for semantic matching in product search. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. pp. 2643–2651 (2021)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16, 321–357 (2002)
Chen, T., Lin, L., Chen, R., Hui, X., Wu, H.: Knowledge-guided multi-label few-shot learning for general image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1371–1384 (2020)
Chen, T., Xu, M., Hui, X., Wu, H., Lin, L.: Learning semantic-specific graph representation for multi-label image recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 522–531 (2019)
Chen, Z.M., Wei, X.S., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5177–5186 (2019)
Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments, and results. IEEE J. Biomed. Health Inform. 21(3), 588–598 (2016)
Cole, E., Mac Aodha, O., Lorieul, T., Perona, P., Morris, D., Jojic, N.: Multi-label learning from single positive labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 933–942 (2021)
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: Practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 702–703 (2020)
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)
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)
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)
Ding, Z., Wang, A., Chen, H., Zhang, Q., Liu, P., Bao, Y., Yan, W., Han, J.: Exploring structured semantic prior for multi label recognition with incomplete labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3398–3407 (2023)
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20(1), 18–36 (2004)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303–338 (2010)
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., Qiao, Y.: Clip-adapter: Better vision-language models with feature adapters. Int. J. Comput. Vision 132(2), 581–595 (2024)
Huang, H., Rawlekar, S., Chopra, S., Deniz, C.M.: Radiology reports improve visual representations learned from radiographs. In: Medical Imaging with Deep Learning. pp. 1385–1405. PMLR (2024)
Ilharco, G., Wortsman, M., Wightman, R., Gordon, C., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., Schmidt, L.: Openclip (Jul 2021). https://doi.org/10.5281/zenodo.5143773, https://doi.org/10.5281/zenodo.5143773, if you use this software, please cite it as below
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904–4916. PMLR (2021)
Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2020)
Karthik, S., Roth, K., Mancini, M., Akata, Z.: Vision-by-language for training-free compositional image retrieval. arXiv preprint arXiv:2310.09291 (2023)
Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 103–112 (2019)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: 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, F., Xiang, T., Hospedales, T.M., Yang, W., Sun, C.: Semantic regularisation for recurrent image annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2872–2880 (2017)
Liu, W., Tsang, I.: On the optimality of classifier chain for multi-label classification. Advances in Neural Information Processing Systems 28 (2015)
Liu, W., Wang, H., Shen, X., Tsang, I.W.: The emerging trends of multi-label learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7955–7974 (2021)
Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39(2), 539–550 (2008)
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)
Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. arXiv preprint arXiv:2007.07314 (2020)
Meyers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., Murphy, K.P.: Im2calories: towards an automated mobile vision food diary. In: Proceedings of the IEEE international conference on computer vision. pp. 1233–1241 (2015)
Misra, I., Lawrence Zitnick, C., Mitchell, M., Girshick, R.: Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2930–2939 (2016)
Park, S., Lim, J., Jeon, Y., Choi, J.Y.: Influence-balanced loss for imbalanced visual classification. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 735–744 (2021)
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
Ridnik, T., Ben-Baruch, E., Zamir, N., Noy, A., Friedman, I., Protter, M., Zelnik-Manor, L.: Asymmetric loss for multi-label classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 82–91 (2021)
Sun, X., Hu, P., Saenko, K.: Dualcoop: Fast adaptation to multi-label recognition with limited annotations. Adv. Neural. Inf. Process. Syst. 35, 30569–30582 (2022)
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: Cnn-rnn: A unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2285–2294 (2016)
Wortsman, M., Ilharco, G., Kim, J.W., Li, M., Kornblith, S., Roelofs, R., Lopes, R.G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al.: Robust fine-tuning of zero-shot models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 7959–7971 (2022)
Wu, X., Fu, X., Liu, Y., Lim, E.P., Hoi, S.C., Sun, Q.: A large-scale benchmark for food image segmentation. In: Proceedings of the 29th ACM international conference on multimedia. pp. 506–515 (2021)
Xu, M., Zhang, Z., Wei, F., Lin, Y., Cao, Y., Hu, H., Bai, X.: A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model. In: European Conference on Computer Vision. pp. 736–753. Springer (2022)
Yang, J., Price, B., Cohen, S., Yang, M.H.: Context driven scene parsing with attention to rare classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3294–3301 (2014)
Yao, Y., Zhang, A., Zhang, Z., Liu, Z., Chua, T.S., Sun, M.: Cpt: Colorful prompt tuning for pre-trained vision-language models. AI Open 5, 30–38 (2024)
Yazici, V.O., Gonzalez-Garcia, A., Ramisa, A., Twardowski, B., Weijer, J.v.d.: Orderless recurrent models for multi-label classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13440–13449 (2020)
Zhang, H., Li, F., Ahuja, N.: Open-nerf: Towards open vocabulary nerf decomposition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3456–3465 (2024)
Zhang, H., Li, F., Qi, L., Yang, M.H., Ahuja, N.: Csl: Class-agnostic structure-constrained learning for segmentation including the unseen. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 7078–7086 (2024)
Zhang, R., Zhang, W., Fang, R., Gao, P., Li, K., Dai, J., Qiao, Y., Li, H.: Tip-adapter: Training-free adaption of clip for few-shot classification. In: European conference on computer vision. pp. 493–510. Springer (2022)
Zhang, Z., Pfister, T.: Learning fast sample re-weighting without reward data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 725–734 (2021)
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337–2348 (2022)
Acknowledgement
We thank Kamila Abdiyeva for her insightful feedback on the manuscript. The support of the Office of Naval Research under grant N00014-20-1-2444, of USDA National Institute of Food and Agriculture under grant 2020-67021-32799/1024178 and Vizzhy.com are gratefully acknowledged.
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
Rawlekar, S., Bhatnagar, S., Srinivasulu, V.P., Ahuja, N. (2025). Improving Multi-label Recognition using Class Co-Occurrence Probabilities. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15310. Springer, Cham. https://doi.org/10.1007/978-3-031-78192-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-78192-6_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-78191-9
Online ISBN: 978-3-031-78192-6
eBook Packages: Computer ScienceComputer Science (R0)