10 citations · 23 across the 18 of their papers we have counts for
12 papers · 1 filter
SketchVLM: Vision language models can annotate images to explain thoughts and guide users
Brandon Collins, Logan Bolton, Hung Huy Nguyen +3
When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models (VLMs) such as Gemini-3-Pro an…
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality
Mohamed Elmoghany, Ryan Rossi, Seunghyun Yoon +26
Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-…
Understanding Generative AI Capabilities in Everyday Image Editing Tasks
Mohammad Reza Taesiri, Brandon Collins, Logan Bolton +4
Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subj…
VideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance
Mohammad Reza Taesiri, Abhijay Ghildyal, Saman Zadtootaghaj +2
With video games now generating the highest revenues in the entertainment industry, optimizing game development workflows has become essential for the sector's sustained growth. Re…
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma +31
Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they a…
VideoGameBunny: Towards vision assistants for video games
Mohammad Reza Taesiri, Cor-Paul Bezemer
Large multimodal models (LMMs) hold substantial promise across various domains, from personal assistance in daily tasks to sophisticated applications like medical diagnostics. Howe…
Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy
Giang Nguyen, Mohammad Reza Taesiri, Sunnie S. Y. Kim +1
Via thousands of papers in Explainable AI (XAI), attention maps \cite{vaswani2017attention} and feature importance maps \cite{bansal2020sam} have been established as a common means…
GlitchBench: Can large multimodal models detect video game glitches?
Mohammad Reza Taesiri, Tianjun Feng, Anh Nguyen +1
Large multimodal models (LMMs) have evolved from large language models (LLMs) to integrate multiple input modalities, such as visual inputs. This integration augments the capacity…
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Giang, Nguyen, Valerie Chen +2
Nearest neighbors (NN) are traditionally used to compute final decisions, e.g., in Support Vector Machines or k-NN classifiers, and to provide users with explanations for the model…
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification
Mohammad Reza Taesiri, Giang Nguyen, Sarra Habchi +2
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…
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Giang Nguyen, Mohammad Reza Taesiri, Anh Nguyen
Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In…
CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning
Mohammad Reza Taesiri, Finlay Macklon, Cor-Paul Bezemer
Gameplay videos contain rich information about how players interact with the game and how the game responds. Sharing gameplay videos on social media platforms, such as Reddit, has…