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Few-shot semantic segmentation (FSS), which can perform segmentation using only a limited number of annotated examples, is a promising technique that has been embedded in many electronic products. Existing approaches usually achieve segmentation for the query image by computing the similarity between the support and query images. However, when segmenting a new query image, the model prediction may be interfered with by distinct classes with similar semantic information, leading to unsatisfactory results. This may greatly weaken the generalization of FSS in real-world scenarios. In response to this challenge, we propose a few-shot semantic segmentation model based on inter-class relation mining named IRMNet. Firstly, we devise a class filter module that accurately selects useful semantic information by mining the class relations between the query and support images. Then, we use a class …