128 citations · 483 across the 31 of their papers we have counts for
5 papers · 1 filter
Is Large-Scale Pretraining the Secret to Good Domain Generalization?
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Multi-Source Domain Generalization (DG) is the task of training on multiple source domains and achieving high classification performance on unseen target domains. Recent methods co…
The Inter-Intra Modal Measure: A Predictive Lens on Fine-Tuning Outcomes in Vision-Language Models
Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis
The fine-tuning of large vision-language foundation models remains an underexplored area, particularly regarding its impact on learning gains and catastrophic forgetting. Inspired…
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP
Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis
Foundations models are presented as generalists that often perform well over a myriad of tasks. Fine-tuning these models, even on limited data, provides an additional boost in task…
Image-Caption Encoding for Improving Zero-Shot Generalization
Eric Yang Yu, Christopher Liao, Sathvik Ravi +2
Recent advances in vision-language models have combined contrastive approaches with generative methods to achieve state-of-the-art (SOTA) on downstream inference tasks like zero-sh…
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap
Christopher Liao, Christian So, Theodoros Tsiligkaridis +1
Domain generalization (DG) is an important problem that learns a model which generalizes to unseen test domains leveraging one or more source domains, under the assumption of share…