GOOSE-M2F model enhances semantic segmentation for rare outdoor terrain classes

Researchers have developed GOOSE-M2F, a specialized version of Mask2Former designed for the GOOSE 2D Fine-Grained Semantic Segmentation (FGSS) Challenge. This new model addresses the challenge of segmenting 64 fine-grained classes in unstructured outdoor terrain, particularly focusing on rare classes with very few pixels. GOOSE-M2F incorporates several enhancements, including an increased number of object queries, a feature refinement module with attention mechanisms, and an auxiliary supervision head to improve gradients for rare classes. The model achieved third place in the challenge with a composite mIoU of 70.08%. AI

IMPACT Enhances fine-grained semantic segmentation capabilities for rare classes in complex outdoor environments.

RANK_REASON The cluster describes a new research paper detailing a novel adaptation of an existing model for a specific computer vision task and challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• ASPP-lite
• Convolutional Block Attention Module
• GitHub
• GOOSE 2D Fine-Grained Semantic Segmentation (FGSS) Challenge
• GOOSE-M2F
• Hugging Face
• ICRA 2026
• Mask2Former
• Nikhileswara Rao Sulake
• Swin-Large

• paper
• model release

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