Clustering-Based Adaptive Query Generation for Semantic Segmentation

Yeong Woo Kim, Wonjun Kim

Abstract: Semantic segmentation is one of the crucial tasks in the field of computer vision, aiming to label each pixel according to its class. Most recently, several semantic segmentation methods, which adopt the transformer decoder with learnable queries, have achieved the impressive improvement. However, since learnable queries are primarily determined by the distribution of training samples, discriminative characteristics of the input image often have been disregarded. In this letter, we propose a novel clustering-based query generation method for semantic segmentation. The key idea of the proposed method is to adaptively generate queries based on the clustering scheme, which leverages semantic affinities in the latent space. By aggregating latent features that represent the same class in a given input, the semantic information of each class can be efficiently encoded into the query. Furthermore, we propose to apply the auxiliary loss function to predict the segmentation result in a coarse scale during the process of query generation. This enables each query to grasp spatial information of the target object in a given image. Experimental results on various benchmarks show that the proposed method effectively improves the performance of semantic segmentation.

External IDs:dblp:journals/spl/KimK25a