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Found 7 papers, 1 papers with code
BRAVE: Broadening the visual encoding of vision-language models
Our results highlight the potential of incorporating different visual biases for a more broad and contextualized visual understanding of VLMs.
Unraveling the Key Components of OOD Generalization via Diversification
We show that (1) diversification methods are highly sensitive to the distribution of the unlabeled data used for diversification and can underperform significantly when away from a method-specific sweet spot.
4M: Massively Multimodal Masked Modeling
Current machine learning models for vision are often highly specialized and limited to a single modality and task.
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
We propose a method for adapting neural networks to distribution shifts at test-time.
3D Common Corruptions and Data Augmentation
We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks.
Robustness via Cross-Domain Ensembles
We present a method for making neural network predictions robust to shifts from the training data distribution.
High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstruction
Spectral imaging is a fundamental diagnostic technique with widespread application.
Rolling Shutter Correction
Vocal Bursts Intensity Prediction
