London AI, Machine Learning and Computer Vision Meetup

Join our virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Feb 5, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

Unlocking Visual Anomaly Detection: Navigating Challenges and Pioneering with Vision-Language Models

Visual anomaly detection (VAD) is pivotal for ensuring quality in manufacturing, medical imaging, and safety inspections, yet it continues to face challenges such as data scarcity, domain shifts, and the need for precise localization and reasoning. This seminar explores VAD fundamentals, core challenges, and recent advancements leveraging vision-language models and multimodal large language models (MLLMs). We contrast CLIP-based methods for efficient zero/few-shot detection with MLLM-driven reasoning for explainable, threshold-free outcomes. Drawing from recent studies, we highlight emerging trends, benchmarks, and future directions toward building adaptable, real-world VAD systems. This talk is designed for researchers and practitioners interested in AI-driven inspection and next-generation multimodal approaches.

About the Speaker

Hossein Kashiani is a fourth-year Ph.D. student at Clemson University. His research focuses on developing generalizable and trustworthy AI systems, with publications in top venues such as CVPR, WACV, ICIP, IJCB, and TBIOM. His work spans diverse applications, including anomaly detection, media forensics, biometrics, healthcare, and visual perception.

Data-Centric Lessons To Improve Speech-Language Pretraining

Spoken Question-Answering (SQA) is a core capability for useful and interactive artificial intelligence systems. Recently, several speech-language models (SpeechLMs) have been released with a specific focus on improving their SQA performance. However, a lack of controlled ablations of pretraining data processing and curation makes it challenging to understand what factors account for performance, despite substantial gains from similar studies in other data modalities. In this work, we address this gap by conducting a data-centric exploration for pretraining SpeechLMs.

We focus on three research questions fundamental to speech-language pretraining data:

• How to process raw web-crawled audio content for speech-text pretraining;
• How to construct synthetic pretraining datasets to augment web-crawled data;
• How to interleave (text, audio) segments into training sequences.

We apply the insights from our controlled data-centric ablations to pretrain a 3.8B-parameter SpeechLM, called SpeLangy, that outperforms models that are up to 3x larger by 10.2% absolute performance. We hope our findings highlight the impact of effective data curation for speech-language pretraining and guide future data-centric exploration in SpeechLMs.

About the Speaker

Vishaal Udandarao is a third year ELLIS PhD student, jointly working with Matthias Bethge at The University of Tuebingen and Samuel Albanie at The University of Cambridge/Google Deepmind. He is also a part of the International Max Planck Research School for Intelligent Systems. He is mainly interested in understanding the generalisation properties of foundation models, both vision-language models (VLMs) and large multi-modal models (LMMs), through the lens of their pre-training and test data distributions. His research is funded by a Google PhD Fellowship in Machine Intelligence.

A Practical Pipeline for Synthetic Data with Nano Banana Pro + FiftyOne

Most computer-vision failures come from the rare cases, the dark corners, odd combinations, and edge conditions we never capture enough in real datasets. In this session, we walk through a practical end-to-end pipeline for generating targeted synthetic data using Google’s Nano Banana Pro and managing it with FiftyOne. We’ll explore how to translate dataset gaps into generation prompts, create thousands of high-quality synthetic images, automatically enrich them with metadata, and bring everything into FiftyOne for inspection, filtering, and validation. By the end, you’ll understand how to build a repeatable synthetic-first workflow that closes real vision gaps and improves model performance on the scenarios that matter most.

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. I started as a software developer, moved into AI, led teams, and served as CTO. Today, I connect code and community to build open, production-ready AI, making technology simple, accessible, and reliable.

Making Computer Vision Models Faster: An Introduction to TensorRT Optimization

Modern computer vision applications demand real-time performance, yet many deep learning models struggle with high latency during deployment. This talk introduces how TensorRT can significantly accelerate inference by applying optimizations such as layer fusion, precision calibration, and efficient memory management. Attendees will learn the core concepts behind TensorRT, how it integrates into existing CV pipelines, and how to measure and benchmark improvements. Through practical examples and performance comparisons, the session will demonstrate how substantial speedups can be achieved with minimal model-accuracy loss. By the end, participants will understand when and how to apply TensorRT to make their CV models production-ready.

About the Speaker

Tushar Gadhiya is a Technical Lead at Infocusp Innovations, specialising in deep learning, computer vision, graph learning, and agentic AI. My experience spans academic research as a PhD holder and industry work, where I have contributed to multiple patents.

Join our virtual Meetup to hear talks from experts on cutting-edge topics at the intersection of Visual AI and video use cases.

Time and Location

Feb 11, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

VIDEOP2R: Video Understanding from Perception to Reasoning

Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning.

In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning.

About the Speaker

Yifan Jiang is a third-year Ph.D. student in the Information Science Institute at the University of Southern California (USC-ISI), advised by Dr. Jay Pujara, focusing on natural language processing, commonsense reasoning and multimodality large language models.

Layer-Aware Video Composition via Split-then-Merge

Split-then-Merge (StM) is a novel generative framework that overcomes data scarcity in video composition by splitting unlabeled videos into separate foreground and background layers for self-supervised learning. By utilizing a transformation-aware training pipeline with multi-layer fusion, the model learns to realistically compose dynamic subjects into diverse scenes without relying on expensive annotated datasets. This presentation will cover the problem of video composition and the details of StM, an approach looking at this problem from a generative AI perspective. We will conclude by demonstrating how StM is working, and outperforming state-of-the-art methods in both quantitative benchmarks and qualitative evaluations.

About the Speaker

Ozgur Kara is a 4th year Computer Science PhD student at the University of Illinois Urbana-Champaign (UIUC), advised by Founder Professor James M. Rehg. His research builds the next generation of video AI by tackling three core challenges: efficiency, controllability, and safety.

Video-native VLMs and control

We show how image-native vision–language models can be extended to support native video understanding, structured reasoning, tool use, and robotics. Our approach focuses on designing data, modeling, and training recipes to optimize for multimodality input and interaction patterns - treating vision and perception as a first class citizens. We discuss lessons learned from scaling these methods in an open-source model family and their implications for building flexible multimodal systems.

About the Speaker

Akshat Shrivastava is the CTO and co-founder of Perceptron, previously leading AR On-Device at Meta and conducting research at UW.

Video Intelligence Is Going Agentic

Video content has become ubiquitous in our digital world, yet the tools for working with video have remained largely unchanged for decades. This talk explores how the convergence of foundation models and agent architectures is fundamentally transforming video interaction and creation. We'll examine how video-native foundation models, multimodal interfaces, and agent transparency are reshaping enterprise media workflows through a deep dive into Jockey, a pioneering video agent system.

About the Speaker

James Le currently leads the developer experience function at TwelveLabs - a startup building foundation models for video understanding. He previously operated in the MLOps space and ran a blog/podcast on the Data & AI infrastructure ecosystem.

In this technical workshop, we’ll show how to build a feedback-driven annotation pipeline for perception models using FiftyOne. We’ll explore real model failures and data gaps, and turn them into focused annotation tasks that then route through a repeatable workflow for labeling and QA. The result is an end-to-end pipeline keeping annotators, tools, and models aligned and closing the loop from annotation, curation, back to model training and evaluation.

Time and Location

Feb 18, 2026
10 - 11 AM PST
Online. Register for the Zoom!

What you'll learn

• Techniques for labeling the data that matters the most for annotation time and cost savings
• Structure human-in-the-loop workflows for finding and fixing model errors, data gaps, and targeted relabeling instead of bulk labeling
• Combine auto-labeling and human review in a single, feedback-driven pipeline for perception models
• Use label schemas and metadata as “data contracts” to enforce consistency between annotators, models, and tools, especially for multimodal data
• Detect and manage schema drift and tie schema versions to dataset and model versions for reproducibility
• QA and review steps that surface label issues early and tie changes back to model behavior
• An annotation architecture that can accommodate new perception tasks and feedback signals without rebuilding your entire data stack

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date and Location

Mar 5, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

MOSPA: Human Motion Generation Driven by Spatial Audio

Enabling virtual humans to dynamically and realistically respond to diverse auditory stimuli remains a key challenge in character animation, demanding the integration of perceptual modeling and motion synthesis. Despite its significance, this task remains largely unexplored. Most previous works have primarily focused on mapping modalities like speech, audio, and music to generate human motion. As of yet, these models typically overlook the impact of spatial features encoded in spatial audio signals on human motion.

To bridge this gap and enable high-quality modeling of human movements in response to spatial audio, we introduce the first comprehensive Spatial Audio-Driven Human Motion (SAM) dataset, which contains diverse and high-quality spatial audio and motion data. For benchmarking, we develop a simple yet effective diffusion-based generative framework for human MOtion generation driven by SPatial Audio, termed MOSPA, which faithfully captures the relationship between body motion and spatial audio through an effective fusion mechanism. Once trained, MOSPA can generate diverse, realistic human motions conditioned on varying spatial audio inputs. We perform a thorough investigation of the proposed dataset and conduct extensive experiments for benchmarking, where our method achieves state-of-the-art performance on this task.

About the Speaker

Zhiyang (Frank) Dou is a Ph.D. student at MIT CSAIL, advised by Prof. Wojciech Matusik. I work with the Computational Design and Fabrication Group and the Computer Graphics Group.

Securing the Autonomous Future: Navigating the Intersection of Agentic AI, Connected Devices, and Cyber Resilience

With billions of devices now in our infrastructure and emerging as autonomous agents (AI), we face a very real question: How can we create intelligent systems that are both secure and trusted? This talk will explore the intersection of agentic AI and IoT and demonstrate how the same AI systems can provide robust defense mechanisms. At its core, however, this is a challenge about trusting people with technology, ensuring their safety, and providing accountability. Therefore, creating a new way of thinking is required, one in which security is built in, and where autonomous action has oversight; and, ultimately, innovation leads to greater human well-being.

About the Speaker

Samaresh Kumar Singh is an engineering principal at HP Inc. with more than 21 years of experience in designing and implementing large-scale distributed systems, cloud native platform systems, and edge AI / ML systems. His expertise includes agentic AI systems, GenAI / LLMs, Edge AI, federated and privacy preserving learning, and secure hybrid cloud / edge computing.

Plugins as Products: Bringing Visual AI Research into Real-World Workflows with FiftyOne

Visual AI research often introduces new datasets, models, and analysis methods, but integrating these advances into everyday workflows can be challenging. FiftyOne is a data-centric platform designed to help teams explore, evaluate, and improve visual AI, and its plugin ecosystem is how the platform scales beyond the core. In this talk, we explore the FiftyOne plugin ecosystem from both perspectives: how users apply plugins to accelerate data-centric workflows, and how researchers and engineers can package their work as plugins to make it easier to share, reproduce, and build upon. Through practical examples, we show how plugins turn research artifacts into reusable components that integrate naturally into real-world visual AI workflows.

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.

Transforming Business with Agentic AI

Agentic AI is reshaping business operations by employing autonomous systems that learn, adapt, and optimize processes independently of human input. This session examines the essential differences between traditional AI agents and Agentic AI, emphasizing their significance for project professionals overseeing digital transformation initiatives. Real-world examples from eCommerce, insurance, and healthcare illustrate how autonomous AI achieves measurable outcomes across industries. The session addresses practical orchestration patterns in which specialized AI agents collaborate to resolve complex business challenges and enhance operational efficiency. Attendees will receive a practical framework for identifying high-impact use cases, developing infrastructure, establishing governance, and scaling Agentic AI within their organizations.

About the Speaker

Joyjit Roy is a senior technology and program management leader with over 21 years of experience delivering enterprise digital transformation, cloud modernization, and applied AI programs across insurance, financial services, and global eCommerce.