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AI models achieve top ranks in ICRA 2026 GOOSE 2D segmentation challenge · 4 sources tracked

By PulseAugur Editorial · [4 sources] ·

Researchers have developed advanced methods for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, achieving top rankings. One team leveraged the Segment Anything Model 3 (SAM3) with a self-distillation technique and multi-scale test-time augmentation, securing 4th place with a 69.73% mIoU. Another group utilized DINOv3 with a Mask2Former decoder and ensemble methods, claiming 1st place with a 76.57% composite score. A third entry, GOOSE-M2F, adapted Mask2Former with specific modules and training strategies to handle long-tailed distributions, achieving 3rd place with a 70.08% mIoU. AI

IMPACT Advances in fine-grained semantic segmentation techniques, particularly for challenging outdoor terrains and long-tailed datasets.

RANK_REASON Multiple research papers detailing entries and methods for a specific academic challenge.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

AI models achieve top ranks in ICRA 2026 GOOSE 2D segmentation challenge · 4 sources tracked

COVERAGE [4]

  1. arXiv cs.CV TIER_1 English(EN) · Xuesong Wang ·

    SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation

    arXiv:2606.20130v1 Announce Type: new Abstract: We describe our 4th-place entry to the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which reached a composite mean Intersection-over-Union (mIoU) of 69.73% on the official 1,815-image test set. Our model adapts t…

  2. arXiv cs.CV TIER_1 English(EN) · Xuesong Wang ·

    SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation

    We describe our 4th-place entry to the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which reached a composite mean Intersection-over-Union (mIoU) of 69.73% on the official 1,815-image test set. Our model adapts the image encoder of a recent visual foundation m…

  3. arXiv cs.CV TIER_1 English(EN) · Sung-Hoon Yoon ·

    Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Leveraging DINOv3 for Robust Outdoor Scene Understanding in Field Robotics

    The GOOSE 2D Fine-Grained Semantic Segmentation Challenge at the ICRA 2026 Workshop on Field Robotics evaluates dense semantic segmentation of off-road imagery over a fine-grained taxonomy of 64 classes and 11 evaluated non-void coarse categories. We present the first-place solut…

  4. arXiv cs.CV TIER_1 English(EN) · Jyothiraditya Lingam, Nikhileswara Rao Sulake, Sai Manikanta Eswar Machara ·

    GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain

    arXiv:2606.15937v1 Announce Type: new Abstract: We present GOOSE-M2F, a task-specific adaptation of Mask2Former for the GOOSE 2D Fine-Grained Semantic Segmentation (FGSS) Challenge at ICRA~2026. The GOOSE benchmark spans 64 fine-grained classes across unstructured outdoor terrain…

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