Prototype-based Causal Intervention for Multi-Label Image Classification

Modern multi-label image classification models suffer from a critical reliance on spurious correlations, failing to learn the underlying causal mechanisms.Many causality-inspired methods are impractical, demanding box-level supervision that is rarely available in real-world datasets.Others rely on static confounder dictionaries, which are inherently inflexible and fail to capture complex biases or adapt to feature space changes during training.To address this, we present prototype-based causal intervention (ProCI), a novel framework that approximates the backdoor adjustment using only image-level supervision. It models confounders as learnable contextual prototypes which, unlike traditional prototypes designed for discriminative features, are engineered to represent class-wise co-occurring bias.These prototypes are learned dynamically within a stable memory and leveraged to construct sample-specific bias vectors for an adaptive feature adjustment, effectively counteracting spurious correlations.Experiments on MS-COCO, Pascal VOC, and the challenging Sewer-ML dataset validate our approach. ProCI achieves competitive performance on standard benchmarks while setting a new state-of-the-art on the highly-confounded Sewer-ML. It outperforms the previous best model by a remarkable +5.44 points on the primary $F2_{CIW}$ metric. These results demonstrate the effectiveness of our approach in mitigating complex real-world biases using only image-level supervision.