Athens AI, Machine Learning and Computer Vision Meetup

Jul 10, 2026
9 AM - 11 AM PT
Online. Register for Zoom!

Advancing Generative Quality and Reasoning in Multimodal AI

This talk exposes hidden limitations of frontier multimodal models across reasoning and visual generation, demonstrates the inherent brittleness of VLMs and audio-visual MLLMs, and introduces simple yet effective techniques to build robustness. It also covers human-centric metrics for perceptually accurate evaluation of generative media.

About the Speaker

Deepti Ghadiyaram is an Assistant Professor of Computer Science at Boston University. Her research focuses on building safe, interpretable, and robust computer vision systems with advanced reasoning capabilities. Before joining BU she was at Runway and Meta AI, and earned her PhD from UT Austin in 2017.

HyperRealm: Hyperbolic Vision Language Models for Real-World Hierarchical Multimodal Understanding

Real-world multimodal data naturally exhibits hierarchical structure, yet standard VLMs like CLIP align images and text in Euclidean space, which cannot preserve tree-like hierarchies. HyperRealm embeds images and text in a Poincaré ball to encode hierarchical relationships, introducing an adaptive entropy-driven entailment loss. Evaluated on 18 zero-shot classification benchmarks, it shows consistent improvements over Euclidean CLIP baselines.

About the Speaker

Kathy Wu holds a Ph.D. in Applied Mathematics from USC. She is currently an Applied Scientist at Amazon within the Global Store organization, leading projects in e-commerce recommendation, multimodal VLMs, and LLM/GenAI applications. Her research has been published at ICCV, CVPR, ICLR, SIGIR, and WACV.

Cross-Modal Domain Adaptation using Semantic Parametric Mapping

XD-MAP is a framework that transfers semantic knowledge from image datasets to LiDAR by constructing semantic parametric maps from monocular detections and geometric priors. Unlike previous approaches, XD-MAP does not require overlapping sensor views and enables scalable 360° supervision for LiDAR perception without manual annotation.

About the Speaker

Frank Bieder is a researcher in computer vision and autonomous systems, leading the Visual and Spatial Learning group at FZI Research Center for Information Technology. His research covers multimodal perception, map-based learning, and cross-sensor domain adaptation for autonomous driving. He received his Ph.D. from KIT in 2026.

WalkGPT: Pixel-Grounded Navigation Guidance for Pedestrians

Pedestrian navigation requires more than generic scene description; users need to understand walkable areas, obstacles, and the distance of surrounding objects. In this talk, I will present WalkGPT, a grounded vision-language model for accessibility-aware pedestrian navigation. WalkGPT connects language reasoning with segmentation masks and object-level distance estimates to generate grounded navigation guidance from pedestrian-view images. I will also introduce PAVE, a 41k-sample benchmark for depth-aware accessibility reasoning in real pedestrian environments. The talk will highlight how grounded multimodal AI can support safer and more interpretable pedestrian assistance.

About the Speaker

Rafi Ibn Sultan is a Ph.D. researcher in Computer Science at Wayne State University, working on computer vision, multimodal AI, and vision-language models. His research focuses on grounded and interpretable AI systems for real-world visual reasoning, including pedestrian navigation and medical image segmentation. His recent work includes WalkGPT, accepted at CVPR 2026, and GeoSAM, accepted at ECAI 2025.

Jul 21, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Outdoor Robot Navigation in the Unstructured World: From Traversability to Physical Scene Understanding

Outdoor robot navigation in the unstructured world requires robots to reason about more than obstacles: they must understand where they can move, what terrain is suitable, and how scene context affects navigation decisions. In sidewalks, campuses, trails, and off-road environments, these decisions depend on geometric structure, terrain conditions, semantic cues, and robot-environment interaction.

In this talk, I will present our recent work on scene understanding for outdoor navigation, including a large-scale multimodal dataset for studying outdoor traversability, approaches for trajectory generation and selection, vision-language reasoning for contextual navigation, and Gaussian-based 3D scene modeling. I will also discuss how physical reasoning can extend scene understanding from visual and geometric perception toward terrain properties and interaction cues.

Together, these works explore how robots can better interpret unstructured outdoor environments and use that understanding for navigation decision-making.

About the Speaker

Jing Liang is a postdoctoral researcher at the Stanford Robotics Center, working on robot navigation, perception, and human-centered autonomy in complex real-world environments.

Scene Graphs and the Future of Mapping

In this talk, I will question whether 3D reconstruction is still a necessary part of mapping in the age of feedforward models and present some alternatives. Then, I discuss scene graphs as an alternative map representation and their applications for mobile manipulation.

About the Speaker

Hermann Blum is a Junior Professor at the University of Bonn and the Lamarr Institute. Hermann's research focuses on machine learning for robotic perception and scene understanding, developing models and methods to understand an agent's environment semantically and geometrically.

Toward Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices

Robust 6D pose estimation of textured objects under diverse illumination conditions remains a significant challenge, often requiring a trade-off between accurate initial pose estimation and efficient real-time tracking. We present a unified framework explicitly designed for efficient execution on edge devices, which fuses a robust initial estimation module with a fast motion-based tracker.

The key to our approach is a shared, lighting-invariant color-pair feature representation that forms a consistent foundation for both stages. For initial estimation, this representation facilitates robust registration between the live RGB-D view and the object's 3D mesh.

For tracking, the same representation validates temporal correspondences, enabling a lightweight model to reliably regress the object's pose. Experiments on benchmark datasets demonstrate that our integrated approach is both effective and robust, providing competitive pose estimation accuracy while maintaining high-fidelity tracking even through abrupt pose changes.

This is joint work with Xingjian Yang.

About the Speaker

Ashis Banerjee is an Associate Professor of Industrial & Systems Engineering and Mechanical Engineering at the University of Washington, Seattle. Prior to joining UW, he was a Research Scientist at GE Global Research and a Postdoctoral Associate at MIT.

Trustworthy Geometric Perception: Certifiable Optimization and Robust Estimation

Autonomous robots in safety-critical settings require geometric perception that is not merely accurate on average, but provably correct under adversarial conditions. Yet most pipelines rely on local optimization methods that fail silently when poorly initialized.

This talk presents GlobustVP, a certifiably optimal vanishing point estimator that reformulates joint VP localization and line association as a quadratically constrained quadratic program (QCQP) and relaxes it to a tight semidefinite program (SDP), achieving the first globally optimal and outlier-robust solution to this problem. Recognized as a Best Paper Award Candidate at CVPR 2025 (top 0.1%, 14 of 13,008 submissions), GlobustVP demonstrates that certifiable global optimization is both practically feasible and highly competitive.

More broadly, this work is part of a research program toward trustworthy geometric perception: systems that know when they are wrong, and can communicate that to the robots and humans that depend on them.

About the Speaker

Zhenjun Zhao I am a postdoctoral researcher at University of Zaragoza, working with Javier Civera.

Jul 22, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Contrastive learning on 3d point clouds for geometric defect detection

Reliable 3D defect detection in manufacturing is hard: the input is a point cloud — an unordered set that standard neural backbones cannot process directly; high-quality training data is scarce; and real scans are noisy and arrive in arbitrary orientations. We address these challenges in COSARAD, a contrastive learning framework that learns highly discriminative representations of object surface geometry under weak supervision.

When a test object arrives, we extract its features and compare them against a library of defect-free reference shapes for precise, interpretable defect localization — achieving state-of-the-art accuracy on industrial benchmarks such as Real3D-AD. In my talk, I'll cover the design choices behind the system, why contrastive representation learning is the right fit for sparse 3D data, and open problems in scaling inspection to production.

About the Speaker

Alexander Tarvo is a researcher at the University of Washington's MACS Lab, where he works on computer vision with applications in robotics. He holds a PhD in Software Engineering from Brown University and previously held research and engineering roles at Google, Microsoft, and IBM Research. His current research focuses on 3D vision and reinforcement learning for industrial robotics.

A Semantic and Occlusion-Aware Gaussian Mixture Probability Hypothesis Density Filter

Reliable and resilient multi-target tracking is foundational for safe autonomous driving, yet most perception pipelines frequently struggle with sensor noise, heavy clutter, and severe environmental occlusions. To resolve these limitations, this talk presents a novel Semantic-Occlusion Aware (S-OA) Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter.

By combining geometric occlusion reasoning with deep learning-derived environmental semantics, the proposed framework adaptively initializes target tracking in regions where new targets are likely to appear. Evaluations demonstrate that this context-aware tracking system minimizes track initiation latency and preserves high tracking precision even under intense clutter.

Ultimately, this work demonstrates how embedding spatial and semantic structure into filtering yields a significantly more robust and resilient perception stack for autonomous navigation.

About the Speaker

Jovan Menezes is a PhD student at Cornell University, advised by Prof. Mark Campbell. His research focuses on developing scalable and resilient perception algorithms for autonomous driving. By leveraging concepts from probabilistic estimation and deep learning-based computer vision, the goal is to enable autonomous vehicles to perceive and navigate in challenging environments.

An Annotation-to-Detection Framework for Autonomous and Robust Vine Trunk Localization in the Field by Mobile Agricultural Robots

Autonomous robots struggle to detect objects in unstructured fields, requiring in-domain tuning with laborious manual data collection. In this work, we introduce a comprehensive annotation-to-detection framework designed to train a robust multi-modal detector using limited and partially labeled training data.

Our method combines cross-modal annotation transfer, early sensor fusion, and a multi-stage detection architecture to train and enhance multi-modal detection. Validated on vineyard trunk detection and paired with a custom LOAM algorithm, it localised over 70% of trees in one pass with under 0.37 m mean error.

Our system demonstrated that robust detection is achievable even with minimal initial annotations and human intervention.

About the Speaker

Dimitrios Chatziparaschis is a PhD candidate in EE, in University of California, Riverside. His main research lies at the intersection of computer vision, machine learning, and robotics. Main topics include 3D perception, multi-modal sensing, landmark detection, and localization in outdoor and dynamic settings.

vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding

We introduce vS-Graphs, a novel real-time VSLAM framework that integrates vision-based scene understanding with map reconstruction and comprehensible graph-based representation. The framework infers structural elements (i.e., rooms and floors) from detected building components (i.e., walls and ground surfaces) and incorporates them into optimizable 3D scene graphs.

This solution enhances the reconstructed map's semantic richness, comprehensibility, and localization accuracy.

About the Speaker

Ali Tourani an R&D Specialist and a Senior Software Engineer with 8+ years of experience in practical computer vision and AI system design and deployment. Currently, he holds a Postdoctoral Research Associate position at the University of Luxembourg, where he develops vision-language models and generative AI solutions for real-world robotic applications.

Jul 23, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity

The domain of automatic video trailer generation is currently undergoing a profound paradigm shift, transitioning from heuristicbased extraction methods to deep generative synthesis. While early methodologies relied heavily on low-level feature engineering, visual saliency, and rule-based heuristics to select representative shots, recent advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), and diffusion-based video synthesis have enabled systems that not only identify key moments but also construct coherent, emotionally resonant narratives.

This survey provides a comprehensive technical review of this evolution, with a specific focus on generative techniques including autoregressive Transformers, LLM-orchestrated pipelines, and text-to-video foundation models like OpenAI's Sora and Google's Veo. We analyze the architectural progression from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT), evaluate the economic implications of automated content velocity on User-Generated Content (UGC) platforms, and discuss the ethical challenges posed by high-fidelity neural synthesis.

By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models, suggesting that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers.

About the Speaker

Abhishek Dharmaratnakar is an Engineering Leader at Google leading YouTube Premium. His work focuses on the intersection of hyperscale media infrastructure and generative artificial intelligence, directing cross-functional engineering organizations to redefine how billions of users consume and create content

Making Agent Systems Observable, Reliable, and Testable

In this talk, I’ll share practical lessons from building real agent systems in computer vision workflows, focusing on how to design evaluation loops, observability pipelines, and sandboxed environments that make agents reliable in practice. We’ll explore how to measure behavior end-to-end, test components independently, and build feedback loops that help agents improve over time, even as tools, models, and pipelines evolve. This talk is for engineers and builders who want to move beyond demos and learn how to make agent systems production-ready.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow, Docker, and OpenCV.

Training-Free Object and Associated Effect Removal in Videos

I will be presenting our recent work, Object-WIPER, which focuses on removing objects and their associated effects from videos. Instead of fine-tuning models for each editing task, our method reuses the priors of pre-trained text-to-video models to perform object and effect removal in a training-free manner. We also curate a real world associated-effect benchmark and evaluation metric for more realistic assessment of video object removal.

About the Speaker

Saksham Singh Kushwaha is a candidate at UT Dallas, with research interests in audio-visual learning, spatial audio, and computer vision. I received my master’s degree from NYU and bachelor’s degree from IIT Delhi.

Turning Models into Systems: AI Architecture That Works

This talk explores what it really takes to make "intelligent systems" work in the messy, high-stakes reality of production environments – not just in demos or pilots. Most AI initiatives do not fail because the algorithms are weak, but because the surrounding system is not designed to handle uncertainty, change, and operational demands.

The session shows how to separate the concerns of building and improving models from their use in daily operations, and how to create a stable core of rules, safety, and business meaning around which smarter components can evolve.

Instead of treating AI as a magic add-on, the talk frames it as a capability that must be grounded in the organization's language, workflows, and responsibilities. It demonstrates how to design that core so that new models, tools, and data sources can be plugged in, compared, and replaced without breaking trust.

Attendees will leave with a clear mental model and a set of practical design ideas for turning clever prototypes into robust, understandable, and adaptable intelligent systems that people on the ground are willing to rely on.

About the Speaker

Dr. Nikita Golovko is a seasoned Solution Architect with over 16 years of experience in designing scalable, secure, and cost-effective software architectures for industrial and business-critical systems.