Publications by Shuqing Teresa Yeo

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We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

Different users find different images generated for the same prompt desirable. This gives rise to personalized image generation which involves creating images aligned with an individual’s visual preference. Current generative models are, however, tuned to ...

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 shift, we create a closed-loop system and make use of test-time feedback sig ...

Visual perception is indispensable for many real-world applications. However, perception models deployed in the real world will encounter numerous and unpredictable distribution shifts, for example, changes in geographic locations, motion blur, and adverse ...

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rate ...

Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occ ...

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. The primary distinction of the proposed transformations is that, unlike e ...

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 of cues (called 'middle domains') and ensembling them into one strong predi ...

In this paper, we present a response to the Interest-Driven Creator (IDC) Theory from a European perspective. Specifically, we raise six questions intended to start a dialogue with respect to IDC Theory’s placement in existing learning theories, its adopti ...

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Shuqing Teresa Yeo

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Controlled Training Data Generation with Diffusion Models

ViPer: Visual Personalization of Generative Models via Individual Preference Learning

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Making Computer Vision Models Robust and Adaptive

Iterative Classroom Teaching

An Analysis of Model Robustness across Concurrent Distribution Shifts

3D Common Corruptions and Data Augmentation

Robustness via Cross-Domain Ensembles

Applying IDC theory to education in the Alps region: A response to Chan et al.’s contribution

4M: Massively Multimodal Masked Modeling