Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
We describe DS@GT's second-place solution to the PlantCLEF 2025 challenge on
multi-species plant identification in vegetation quadrat images. Our pipeline
combines (i) a fine-tuned Vision Transformer ViTD2PC24All for patch-level
inference, (ii) a 4x4 tiling strategy that aligns patch size with the network's
518x518 receptive field, and (iii) domain-prior adaptation through PaCMAP +
K-Means visual clustering and geolocation filtering. Tile predictions are
aggregated by majority vote and re-weighted with cluster-specific Bayesian
priors, yielding a macro-averaged F1 of 0.348 (private leaderboard) while
requiring no additional training. All code, configuration files, and
reproducibility scripts are publicly available at
https://github.com/dsgt-arc/plantclef-2025.
