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Found 7 papers, 6 papers with code
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters.
Ranked #14 on
Visual Question Answering (VQA)
on InfiMM-Eval
Are aligned neural networks adversarially aligned?
We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force.
Out-of-Domain Robustness via Targeted Augmentations
Models trained on one set of domains often suffer performance drops on unseen domains, e. g., when wildlife monitoring models are deployed in new camera locations.
CREPE: Can Vision-Language Foundation Models Reason Compositionally?
To measure systematicity, CREPE consists of a test dataset containing over $370K$ image-text pairs and three different seen-unseen splits.
Adaptive Testing of Computer Vision Models
Vision models often fail systematically on groups of data that share common semantic characteristics (e. g., rare objects or unusual scenes), but identifying these failure modes is a challenge.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
