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Found 8 papers, 5 papers with code
EvalGIM: A Library for Evaluating Generative Image Models
To enable actionable evaluation insights, we introduce ''Evaluation Exercises'' that highlight takeaways for specific evaluation questions.
Consistency-diversity-realism Pareto fronts of conditional image generative models
Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as world simulators.
An Introduction to Vision-Language Modeling
Then, we present and discuss approaches to evaluate VLMs.
Improving Text-to-Image Consistency via Automatic Prompt Optimization
In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models.
Improving Automatic VQA Evaluation Using Large Language Models
Thus, there is a need to develop more robust automatic VQA metrics that serve as a proxy for human judgment.
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting
Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks.
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Transfer learning approaches can reduce the data requirements of deep learning algorithms.
Ranked #4 on
Change Detection
on OSCD - 13ch
(using extra training data)
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images.
