New Framework
Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.
Papers
AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
Giacomo Zara, Subhankar Roy, Paolo Rota, Elisa Ricci
Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach
Jérôme Rutinowski, Hazem Youssef, Sven Franke, Irfan Fachrudin Priyanta, Frederik Polachowski, Moritz Roidl, Christopher Reining
Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation
Leila Ismail, Muhammad Danish Waseem
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
Mahdi Taheri, Mohammad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab, Jaan Raik, Mikael Sjodin, Bjorn Lisper
A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing
Prasun Roy, Subhankar Ghosh, Umapada Pal
ISLE: A Framework for Image Level Semantic Segmentation Ensemble
Erik Ostrowski, Muhammad Shafique