Research

TRINS: Towards Multimodal Language Models that Can Read

TRINS is a dataset of 39K+ text-rich images with detailed captions and QA pairs, built using Quantumworks Lab for high-quality annotation—enabling LaRA, a compact vision-language model that outperforms larger systems on reading and reasoning tasks.

HUM-CARD: A human crowded annotated real dataset

The HUM-CARD study introduces a richly annotated dataset of densely crowded urban scenes to advance crowd analysis and environmental impact research, using Quantumworks Lab to ensure high-quality, scalable human annotations.

Quantumworks Lab services helps research team explore AI-guided defect detection techniques

At Quantumworks Lab, we're passionate about empowering the AI community, especially universities and cutting-edge research teams. While we often share updates on our platform and customer stories, we're excited to introduce a new series of blog posts highlighting fascinating research projects leveraging Labelbox. These posts will delve into how researchers are using Quantumworks Lab—both our software and services—to tackle complex data challenges and push the boundaries of AI. Today, we're exploring AI-Guided

A benchmark for long-form medical question answering

Researchers from Dartmouth University recently advanced the need for comprehensive evaluation benchmarks for large language models (LLMs) in the medical domain.

Identifying and counting avian blood cells in whole slide images via deep learning

Learn how researchers presented a novel approach to automatically quantify avian red and white blood cells in whole slide images, based on two deep neural network models.

NASA/JPL: Onboard instruments for the detection of microscopy biosignatures

Learn how researchers from NASA/JPL are advancing biosignature detection which offers insights and lessons for future mission concepts exploring life in the outer solar system.

Detecting feature requests of 3rd-party developers via machine learning: A case study of the SAP Community

Researchers from the Technical University of Munich, University of Innsbruck and SAP Deutschland set out to test whether the use of supervised machine learning models can be an effective means for the identification of feature requests.

Real-time segmentation of desiccation cracks onboard UAVs for planetary exploration

Researchers from Queensland University of Technology studied the use of Unmanned Aerial Vehicles (UAVs) to detect and highlight areas with desiccation cracks for closer inspection to look for habitable environments and traces of life (biosignatures).

Fruit flower detection in apple orchards using ML

Researchers from the University of Guleph recently demonstrated the effective use of machine vision and learning technologies to support the development of smart agriculture.

Automated recognition of cricket batting techniques in videos using deep learning

Researchers from the University of Johannesburg recently studied how complex batting techniques in cricket can be more efficiently analyzed using machine learning.