About This Project

AI models trained on standard datasets like ImageNet and COCO rely on static images limiting the ability to recognize objects and understand real-world contexts. We hypothesize integrating real-world videos with structured metadata and multi-modal inputs will enhance AI’s object recognition and contextual understanding. The study tests AI performance improvements via structured metadata and multi-modal inputs with future fine-tuning and vector search (stretch goal) to further refine adaptability

Ask the Scientists

What is the context of this research?

Conventional AI training datasets lack real-world diversity, limiting their adaptability to changing environments, human interactions, and dynamic video content (Koh et al., 2021). ImageNet & COCO, widely used datasets, contain only static images, making them insufficient for training AI models to understand moving objects and evolving contexts. Inspired by LIMO, Less Is More for Reasoning (Zhou et al., 2024), which demonstrated that fine-tuning a large model on a smaller, structured dataset can outperform training on vast unstructured datasets, our study tests whether a curated, multimodal dataset of real-world videos improves AI adaptability more efficiently. By integrating structured metadata, time-synchronized labels, and multimodal inputs (video, text, audio), we will evaluate whether these enhancements improve AI’s ability to identify objects, understand context, and interact intelligently within videos (Zhou et al., 2025).

What is the significance of this project?

AI is widely used in environmental monitoring, interactive media, and autonomous systems, yet reliance on outdated, static datasets limits effectiveness (Goodfellow et al., 2016). If AI models fine-tuned with real-world, citizen-contributed videos outperform conventional models, this research could reshape AI training. Recognizing objects in diverse conditions could enhance applications like wildlife conservation, disaster response, and immersive media (Russakovsky et al., 2015). Testing metadata structuring and multimodal interactions also contributes to AI interpretability, improving transparency (Sun et al., 2024). By demonstrating how structured, high-quality video data enhances AI learning over large, unstructured datasets, this study builds on LIMO’s findings that domain-specific structured fine-tuning yields superior performance with less data (Zhou et al., 2024). If validated, our research could redefine best practices in AI training, prioritizing quality over volume.

What are the goals of the project?

Our goal is to evaluate whether real-world video contributions improve AI's ability to recognize objects and understand context beyond conventional datasets (Zhu et al., 2020).

We will:

Assess how structured metadata enhances AI accuracy: We will compare AI models trained with and without structured metadata (COCO-style object labels, time-sync tags) to measure improvements in recognition accuracy (Liu et al., 2018).

Evaluate the impact of multi-modal inputs: We will test video-only vs. multi-modal (video, text, audio) models to measure response accuracy and contextual awareness (Jaegle et al., 2022).

Benchmark AI performance using metrics: We will use mAP for object detection, BLEU/ROUGE for response accuracy, and F1-score for contextual understanding (Lin et al., 2014).

Test fine-tuning on real-world videos (Stretch Goal): If funded, we will fine-tune models on a structured dataset, comparing adaptability against large, unstructured datasets (Zhou et al., 2024; Tan & Le, 2019).