4 citations · 4 across the 4 of their papers we have counts for
7 papers
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
Mingqiao Ye, Zhaochong An, Zhitong Gao +11
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly…
Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality
Kunal Pratap Singh, Ali Garjani, Rishubh Singh +6
Cross-modal learning, i.e., learning to predict one modality from another, is a fundamental mechanism for self-supervision via leveraging multimodality. Many practical applications…
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
Teresa Yeo, Oğuzhan Fatih Kar, Zahra Sodagar +1
We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the…
3D Common Corruptions and Data Augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov +1
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 network…
Robustness via Cross-Domain Ensembles
Teresa Yeo, Oğuzhan Fatih Kar, Alexander Sax +1
We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set…
Robust Learning Through Cross-Task Consistency
Amir Zamir, Alexander Sax, Teresa Yeo +6
Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independen…