# Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification

arXiv: https://arxiv.org/abs/2507.06093 · HTML: https://mlsanity.com/paper/2507.06093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Adaptive Token Sampling For Efficient Vision Transformers](https://mlsanity.com/paper/2111.15667/) (2021)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation](https://mlsanity.com/paper/2312.02284/) (2023)
- [ViTAR: Vision Transformer with Any Resolution](https://mlsanity.com/paper/2403.18361/) (2024)
- [PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts](https://mlsanity.com/paper/2308.01313/) (2023)
- [Win-Win: Training High-Resolution Vision Transformers from Two Windows](https://mlsanity.com/paper/2310.00632/) (2023)
- [Multi-Label Plant Species Classification with Self-Supervised Vision Transformers](https://mlsanity.com/paper/2407.06298/) (2024)
- [Bayesian Exploration of Pre-trained Models for Low-shot Image Classification](https://mlsanity.com/paper/2404.00312/) (2024)
- [[CLS] Token is All You Need for Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2304.06212/) (2023)

## Built on by (most cited first, 2 of 2)

- [Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification](https://mlsanity.com/paper/2607.14509/) (2026)
- [Decoupled Pipeline with Proposal Reranking and Score Fusion for Positive-Unlabeled Marine Species Detection](https://mlsanity.com/paper/2607.18700/) (2026)

## Abstract (arXiv)

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.