# First Place Solution to the MLCAS 2025 GWFSS Challenge: The Devil is in the Detail and Minority

arXiv: https://arxiv.org/abs/2508.17305 · HTML: https://mlsanity.com/paper/2508.17305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2201.00520/) (2022)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [Learning to Upsample by Learning to Sample](https://mlsanity.com/paper/2308.15085/) (2023)
- [Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods](https://mlsanity.com/paper/2005.02162/) (2020)
- [SAPA: Similarity-Aware Point Affiliation for Feature Upsampling](https://mlsanity.com/paper/2209.12866/) (2022)
- [FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling](https://mlsanity.com/paper/2207.10392/) (2022)
- [Guided Distillation for Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2308.02668/) (2023)
- [FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures](https://mlsanity.com/paper/2407.13500/) (2024)

## Built on by (most cited first, 1 of 1)

- [DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data](https://mlsanity.com/paper/2601.12366/) (2026)

## Abstract (arXiv)

In this report, we present our solution during the participation of the MLCAS 2025 GWFSS Challenge. This challenge hosts a semantic segmentation competition specific to wheat plants, which requires to segment three wheat organs including the head, leaf, and stem, and another background class. In 2025, participating a segmentation competition is significantly different from that in previous years where many tricks can play important roles. Nowadays most segmentation tricks have been well integrated into existing codebases such that our naive ViT-Adapter baseline has already achieved sufficiently good performance. Hence, we believe the key to stand out among other competitors is to focus on the problem nature of wheat per se. By probing visualizations, we identify the key -- the stem matters. In contrast to heads and leaves, stems exhibit fine structure and occupy only few pixels, which suffers from fragile predictions and class imbalance. Building on our baseline, we present three technical improvements tailored to stems: i) incorporating a dynamic upsampler SAPA used to enhance detail delineation; ii) leveraging semi-supervised guided distillation with stem-aware sample selection to mine the treasure beneath unlabeled data; and iii) applying a test-time scaling strategy to zoom in and segment twice the image. Despite being simple, the three improvements bring us to the first place of the competition, outperforming the second place by clear margins. Code and models will be released at https://github.com/tiny-smart/gwfss25.