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Giang Nguyen

6 accepted papers

2025

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia

ACL 2025long

Despite Southeast Asia’s (SEA) extraordinary linguistic and cultural diversity, the region remains significantly underrepresented in vision-language (VL) research, resulting in AI models that inadequately capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative de…

2025

Generating Actionable Robot Knowledge Bases by Combining 3D Scene Graphs with Robot Ontologies

IROS 2025

In robotics, the effective integration of environ-mental data into actionable knowledge remains a significant challenge due to the variety and incompatibility of data formats commonly used in scene descriptions, such as MJCF, URDF, and SDF. This paper presents a novel approach that addresses these c

Cited by 0SourceScholar
2025

WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world scenarios

NeurIPS 2025poster

We introduce WearVQA, the first benchmark specifically designed to evaluate the visual question answering (VQA) capabilities of multi-modal AI assistant on wearable devices like smart glasses. Unlike prior benchmarks that focus on high-quality, third-person imagery, WearVQA reflects the unique chal-…

Cited by 0SourceScholar
2023

ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification

NeurIPS 2023poster

Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers to reach high accuracy is to first zoom to the most discriminative region in the image and then extract features from ther…

2022

Visual correspondence-based explanations improve AI robustness and human-AI team accuracy

NeurIPS 2022accept

Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stake applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of explainable image classifiers that first explain, and then predict (as…

2021

The effectiveness of feature attribution methods and its correlation with automatic evaluation scores

NeurIPS 2021poster

Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. Hundreds of papers have either proposed new feature attribution methods, discussed or harnessed these tools in their work. However, despite humans being the target…