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Found 4 papers, 2 papers with code
Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision
In this work, we demonstrate that diffusion models can be leveraged to create such a dataset.
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis
In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures.
Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language
We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs).
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4
However, in this paper, we provide evidence against the unexpected trend of Vision Transformers (ViT) being not perceptually aligned with human visual representations by showing how a dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a joint rotationally-invariant and adversarial optimization procedure yields 2nd place in the aggregate Brain-Score 2022 competition(Schrimpf et al., 2020b) averaged across all visual categories, and at the time of the competition held 1st place for the highest explainable variance of area V4.
