Jack Fitzgerald

Goeric Huybrechts, Srikanth Ronanki, Sai Muralidhar Jayanthi et al.

The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To address this, we introduce Document Haystack, a comprehensive benchmark designed to evaluate the performance of Vision Language Models (VLMs) on long, visually complex documents. Document Haystack features documents ranging from 5 to 200 pages and strategically inserts pure text or multimodal text+image "needles" at various depths within the documents to challenge VLMs' retrieval capabilities. Comprising 400 document variants and a total of 8,250 questions, it is supported by an objective, automated evaluation framework. We detail the construction and characteristics of the Document Haystack dataset, present results from prominent VLMs and discuss potential research avenues in this area.

Changsoo Jung, Sheikh Mannan, Jack Fitzgerald et al.

Interactive and spatially aware technologies are transforming educational frameworks, particularly in K-12 settings where hands-on exploration fosters deeper conceptual understanding. However, during collaborative tasks, existing systems often lack the ability to accurately capture real-world interactions between students and physical objects. This issue could be addressed with automatic 6D pose estimation, i.e., estimation of an object's position and orientation in 3D space from RGB images or videos. For collaborative groups that interact with physical objects, 6D pose estimates allow AI systems to relate objects and entities. As part of this work, we introduce FiboSB, a novel and challenging 6D pose video dataset featuring groups of three participants solving an interactive task featuring small hand-held cubes and a weight scale. This setup poses unique challenges for 6D pose because groups are holistically recorded from a distance in order to capture all participants -- this, coupled with the small size of the cubes, makes 6D pose estimation inherently non-trivial. We evaluated four state-of-the-art 6D pose estimation methods on FiboSB, exposing the limitations of current algorithms on collaborative group work. An error analysis of these methods reveals that the 6D pose methods' object detection modules fail. We address this by fine-tuning YOLO11-x for FiboSB, achieving an overall mAP_50 of 0.898. The dataset, benchmark results, and analysis of YOLO11-x errors presented here lay the groundwork for leveraging the estimation of 6D poses in difficult collaborative contexts.