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Improving Multi-label Recognition using Class Co-Occurrence Probabilities

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15310))

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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.

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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.

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Correspondence to Samyak Rawlekar .

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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

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