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Found 5 papers, 2 papers with code
Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
Furthermore, prior work's Typographic attacks against CLIP randomly sample a misleading class from a predefined set of categories.
From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Bias
By training on real and synthetic data separately, FFR avoids the issue of bias toward signals from the pair $(B, G)$.
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation
Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples.
From Coarse to Fine-grained Concept based Discrimination for Phrase Detection
Phrase detection requires methods to identify if a phrase is relevant to an image and localize it, if applicable.
Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics
We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors.
