Call for Abstracts
Call for Papers
52nd IEEE Applied Imagery Pattern Recognition Workshop
Sept 27-29th, 2023
Saint Louis, Missouri, USA
Deep Learning Using Synthetic, Augmented, and Natural Datasets
AIPR continues the half-century of success and tradition in pioneering new topics in applied image and visual understanding. Forbes estimates that artificial intelligence (AI) will become a $150 trillion dollar industry. AI is impacting nearly every facet of life and with the advent of large language models and transformers for dialog (i.e. Generative Pre-trained Transformer (GPT), Bard) and novel image or video generation (i.e. Dall-E) it will likely redefine our world faster than previous advances in computational learning. While machine learning (ML) and deep learning (DL) is heavily anchored in supervised learning, recent algorithms (e.g., ChatGPT, DALL-E, etc.) are using self-supervised, transfer, and reinforcement learning. However, all these approaches are data intensive. Where does the data and its associated truth/metadata come from? While simulation has been around for decades, what’s new is a convergence in the maturity, realism, and availability of relatively simple-to-use tools and content/assets for individuals who are not computer graphics, physics, nor gaming experts. Companies like Epic Games, Google, Microsoft, Meta, OpenAI, Apple, NVIDIA, IBM, Tesla, Scale AI, and others have taken this a step further and developed billion-dollar in-house solutions based on synthetic data-driven AI. The 2023 IEEE AIPR Workshop will explore AI/ML/DL in synthetic, augmented, and natural datasets. In addition to papers on regular AIPR topics in applied imagery, as they pertain to computer vision, imaging, and pattern recognition, the Workshop Committee invites papers focused on, but not limited to, the following:
Theories, frameworks, and workflows to generate synthetic, augmented, and/or natural datasets;
Novel solutions for interfacing synthetic, augmented, and/or natural datasets;
Digital twins, Omniverse, Metaverse, etc. for representation, modeling, simulation
Neural radiance fields (NeRFs) for virtual environments
AI/ML/DL evaluated on synthetic, augmented, and/or natural datasets
Synthetic or augmented approaches for training AI/ML/DL algorithms
Synthetic data sets for training AI/ML/DL in biomedical imaging, medicine, healthcare, life science
Trustworthy and safe medical AI
Zero shot, few shot learning, domain generalization using synthetic datasets
Verification and validation (V&V); uncertainty quantification; responsible open source AI, trustworthy AI
Generative techniques, large language models, transformers
Simulation of multispectral imagery or non-traditional sensor data
Fusion of multisource imagery data (SAR, optical, thermal, and LiDAR)
Fusion of synthetic, augmented, and/or real data at the data, signal, feature, and/or algorithm level
Transferring simulated/augmented datasets and/or AI/ML/DL models to real data
Approaches for generating accurate and dense truth and metadata
Controlled synthetic/augmented studies that go beyond what is practical or possible in the real-world
Explainable AI (XAI) and evaluating/characterization/understanding of AI/ML/DL algorithms
Applications in remote sensing including agriculture, climate security, arctic navigability, etc.
Scalable approaches for computer vision, change detection, structure from motion, merging real objects with virtual worlds, etc.
Ways to produce, structure, store, and format synthetic/augmented data for AI/ML/DL;
Synthetic/augmented/natural datasets for autonomous vehicles, clinical medical imaging;
Human-in-the-loop (HITL) or human-over-the-loop (HOTL) simulation;
Closing the loop and inverse design
Deadline for abstracts: August 6th, 2023 . The Workshop will include oral and poster presentations, several keynote talks that provide in-depth overviews of the fields, and a special session on the theme topic. Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore's scope and quality requirements. AIPR 2023, the 52nd annual workshop, is sponsored by the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence, and organized by the AIPR Workshop Committee with generous support from sponsors.
Abstract Deadline: August 6th, 2023
2023 Conference Chairs: Andrew Kalukin and
Kannappan Palaniappan (University of Missouri), Michelle Quirk
2023 Program Chairs: Derek Anderson (University of Missouri) and Vasit Sagan (Saint Louis University)