128 citations · 483 across the 31 of their papers we have counts for
9 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…
Robust Fine-Tuning of Vision-Language Models for Domain Generalization
Kevin Vogt-Lowell, Noah Lee, Theodoros Tsiligkaridis +1
Transfer learning enables the sharing of common knowledge among models for a variety of downstream tasks, but traditional methods suffer in limited training data settings and produ…
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Over the past year, a large body of multimodal research has emerged around zero-shot evaluation using GPT descriptors. These studies boost the zero-shot accuracy of pretrained VL m…
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration
Ryan Soklaski, Michael Yee, Theodoros Tsiligkaridis
Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution. However, the ability t…
Active query-driven visual search using probabilistic bisection and convolutional neural networks
Athanasios Tsiligkaridis, Theodoros Tsiligkaridis
We present a novel efficient object detection and localization framework based on the probabilistic bisection algorithm. A Convolutional Neural Network (CNN) is trained and used as…