# CLIP-Guided Multi-Task Regression for Multi-View Plant Phenotyping

arXiv: https://arxiv.org/abs/2603.04091 · HTML: https://mlsanity.com/paper/2603.04091/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Contrastive Test-Time Adaptation](https://mlsanity.com/paper/2204.10377/) (2022)
- [Teach CLIP to Develop a Number Sense for Ordinal Regression](https://mlsanity.com/paper/2408.03574/) (2024)
- [Leaf Counting with Deep Convolutional and Deconvolutional Networks](https://mlsanity.com/paper/1708.07570/) (2017)
- [Leveraging multiple datasets for deep leaf counting](https://mlsanity.com/paper/1709.01472/) (2017)
- [GroMo: Plant Growth Modeling with Multiview Images](https://mlsanity.com/paper/2503.06608/) (2025)
- [Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge](https://mlsanity.com/paper/2404.11230/) (2024)
- [ViewSparsifier: Killing Redundancy in Multi-View Plant Phenotyping](https://mlsanity.com/paper/2509.08550/) (2025)

## Abstract (arXiv)

Modeling plant growth dynamics plays a central role in modern agricultural research. However, learning robust predictors from multi-view plant imagery remains challenging due to strong viewpoint redundancy and viewpoint-dependent appearance changes. We propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings. Our method aggregates rotational views into angle-invariant representations and conditions visual features on lightweight text priors encoding viewpoint level for stable prediction under incomplete or unordered inputs. On the GroMo25 benchmark, our approach reduces mean age MAE from 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The models and code is available at: https://github.com/SimonWarmers/CLIP-MVP