We design an open-vocabulary image segmentation model to organize an image into
meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

In this work, we develop intuitive controls for editing the style of 3D objects. Our framework,
Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which …

As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly
prevalent and seeing growing adoption as commercial services, characterizing the social …

Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …

Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text
representations that have found numerous applications, from zero-shot classification to text …

As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly
prevalent and seeing growing adoption as commercial services, characterizing the social …

Conditioned and composed image retrieval extend CBIR systems by combining a query
image with an additional text that expresses the intent of the user, describing additional …

Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

Text-to-image generative models often reflect the biases of the training data, leading to
unequal representations of underrepresented groups. This study investigates inclusive text …