The paper studies how prompt quality affects vision‑language models in the agrifood domain. It evaluates Zero‑Shot Prompt Ensembling (ZPE) on CLIP and SigLIP across four datasets, showing that ZPE offers limited gains on in‑distribution data but significantly improves accuracy and calibration when the domain shifts. The authors also introduce Prompt‑based Inconsistency Detection (PID), which uses prompt disagreement to detect failures under severe domain shift, outperforming standard confidence measures.
arXiv:2508. 17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis.
arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...
To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding. TomaMMU comprises 28,808 high-quality images spanning 15 categories and 213,119 human-annotated visual question-answer pairs, generated through a three-stage pipeline comprising Data Collection, Human Annotation, and Question-Answer Generation.
arXiv:2609.09417v1 Announce Type: new Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...
arXiv:2607. 18695v1 Announce Type: cross Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors.
arXiv:2608. 08727v1 Announce Type: cross Abstract: To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding.
arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.
The paper introduces VPRef, the first cross‑domain benchmark for Referring Remote Sensing Image Segmentation, containing 46,972 language‑image‑annotation triplets with a three‑tier linguistic hierarchy. It proposes a parameter‑efficient adaptation method based on the Segment Anything Model and Low‑Rank Adaptation, using pseudo‑label self‑training for visual drift and random multi‑granularity prompt mixing for textual drift. Experiments show the approach improves cross‑domain segmentation while altering only 1.08 % of the base model’s parameters, offering a strong baseline for future research.
arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.
TecoPrompt introduces a closed‑loop robust prompt‑learning framework that mitigates label noise in vision‑language models. It uses optimal transport in the CLIP semantic space to generate globally consistent pseudo‑labels, then verifies their reliability by checking trajectory stability over a K‑epoch window and an EMA‑based confidence gate. The verified labels are incorporated into prompt training via a tri‑group objective, yielding significant accuracy gains across multiple noisy datasets, such as a 0.843 accuracy on OxfordPets with 50% asymmetric noise.
Rapid advancements in vision-language models have propelled Referring Remote Sensing Image Segmentation (RRSIS) to the forefront of Earth observation. However, practical deployments suffer severe perf...
Prompt learning modifies vision‑language models by optimizing continuous prompt vectors, yet the resulting prompts are hard to interpret in natural language. PromptSpLiCE is a post‑hoc method that rewrites each class‑conditioned text embedding as a sparse mix of concepts from a fixed dictionary, enabling a direct comparison of concept profiles before and after prompt learning. Across 11 image‑classification datasets, the method shows that only about 1.6 of the initial top‑10 concepts remain after learning, and that larger profile changes correlate with higher accuracy gains, while a derived gradient expression offers geometric insight into loss sensitivity.