Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. View more CV events here.

This talk will explore practical workflows for building, evaluating, and improving modern computer vision systems. We’ll dive into real-world approaches to dataset curation, model analysis, multimodal AI workflows, and production-ready vision pipelines using open-source technologies.

The session is designed for engineers, researchers, and AI practitioners looking to better understand how teams are developing and scaling computer vision applications today. Expect practical demos, technical insights, and discussions around the evolving AI tooling ecosystem.

Modern AI systems require infrastructure that can reliably orchestrate training, inference, and production workflows at scale. This session explores approaches to AI orchestration, distributed compute, and resilient ML infrastructure for real-world machine learning and computer vision applications.

Topics may include production AI pipelines, workflow automation, scalable deployment strategies, and operating AI systems securely within cloud environments. Attendees will gain a high-level look at emerging patterns shaping the next generation of AI infrastructure and operational workflows.

Modern vision models often split into two regimes: models that learn strong semantics for recognition, and models that preserve spatial detail for reconstruction.

In this talk, we present STELLAR, a self-supervised framework for learning sparse visual concepts as a unified representation for vision models. The key idea is to factorize visual features into semantic concept tokens (the "what"), and spatial assignment maps (the "where"), allowing the model to align concepts across views while preserving the geometry needed for reconstruction.

This sparse, low-rank representation creates a compact interface that supports recognition, dense prediction, and image reconstruction, while also suggesting future directions for efficient visual encoding, video self-supervision, generative modeling, and world-model-style visual reasoning.

We discuss the core method, empirical results, and why concept-centric visual representations may be a useful building block for the next generation of unified vision systems.