PlantMicro benchmark shows current VLMs achieve low accuracy (e.g. GPT-5 at 34.93% on pathogen classification) on fine-grained microscopic plant image tasks.
Track any peppers: Weakly supervised sweet pepper tracking using vlms
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PS-Track sets a new state-of-the-art for point-supervised multi-object tracking by converting point seeds into temporally consistent pseudo-labels via Temporal-Feedback Prompting, Point-Excited Wavelet Attention, and Uncertainty-Guided Gaussian Learning.
citing papers explorer
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Benchmarking Vision-Language Models for Microscopic Plant Image Understanding
PlantMicro benchmark shows current VLMs achieve low accuracy (e.g. GPT-5 at 34.93% on pathogen classification) on fine-grained microscopic plant image tasks.
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PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking
PS-Track sets a new state-of-the-art for point-supervised multi-object tracking by converting point seeds into temporally consistent pseudo-labels via Temporal-Feedback Prompting, Point-Excited Wavelet Attention, and Uncertainty-Guided Gaussian Learning.