Anh Totti Nguyen

Users browsing the web daily struggle to quickly locate relevant information in cluttered pages, complete unfamiliar multi-step tasks, and stay...

When answering questions about images, humans naturally point, label, and draw to explain their reasoning.

The reasoning capabilities of Large Language Models (LLMs) have led to their increasing employment in several critical applications, particularly education,...

Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on...

Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that help them on downstream tasks...

Multi-head self-attention (MHSA) is a key component of Transformers, a widely popular architecture in both language and vision.

Interpretability for Table Question Answering (Table QA) is critical, particularly in high-stakes industries like finance or healthcare.

Detecting object-level changes between two images across possibly different views is a core task in many applications that involve visual...

An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate nonfactual statements.

Large language models (LLMs) often exhibit strong biases, e.g, against women or in favor of the number 7.

Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level.

Nearest neighbors (NN) are traditionally used to compute final decisions, e.g., in Support Vector Machines or k-NN classifiers, and to...

While large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 Pro, are powering various image-text applications and scoring...

* Equal contribution.

Large multimodal models (LMMs) have evolved from large language models (LLMs) to integrate multiple input modalities, such as visual inputs....