VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.
CVPR , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
MPD reduces hallucinations in LVLMs by 23.4% while retaining 97.4% of general capability through semantic disentanglement and selective parameter updates.
Energy-based fine-tuning outperforms other OOD detection methods on the real-world Plant Pathology 2021 dataset, improving detection over softmax while maintaining in-distribution accuracy.
citing papers explorer
-
From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.
-
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
MPD reduces hallucinations in LVLMs by 23.4% while retaining 97.4% of general capability through semantic disentanglement and selective parameter updates.
-
Beyond Toy Benchmarks: A Systematic Evaluation of OOD Detection Methods For Plant Pathology Classification
Energy-based fine-tuning outperforms other OOD detection methods on the real-world Plant Pathology 2021 dataset, improving detection over softmax while maintaining in-distribution accuracy.