EXPERIMENTAL STUDY OF DISTANCE MAP DECODER ARCHITECTURAL CONFIGURATIONS FOR INSTANCE SEGMENTATION BY TEXT QUERY
DOI:
https://doi.org/10.34185/1562-9945-2-163-2026-01Keywords:
instance segmentation, distance decoder, PixelShuffle, coordinate convolution, feature fusion, CLIP, open-vocabulary segmentation, InstanceCLIPSegAbstract
This article presents an experimental study of the architectural configurations of distance map decoder in the InstanceCLIPSeg model for instance segmentation by text query. We in-vestigate the influence of various mechanisms for restoring spatial resolution (bilinear inter-polation, PixelShuffle), the use of coordinate convolutions (CoordConv), and multi-level feature fusion strategies. Based on the evaluation of nine configurations on the LVIS and PhraseCut datasets, it was found that a hybrid architecture with PixelShuffle and single-stage feature fusion from transformer layers achieves the best results (mean Dice 0.2374), outper-forming baseline approaches. The redundancy of coordinate channels in the presence of mul-ti-level fusion was revealed.
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