July 28 - Paris AI, ML, and CV Meetup

Join us for an in-person Meetup to hear talks from experts on cutting-edge topics across AI, ML and computer vision.

Date, Time and Location

Jul 28, 2026
5:30 PM - 8:30 PM CEST

InstaDeep SAS
42 Rue de Paradis
75010 Paris, France

Finetuning VLMs for domain specific tasks

Vision-language models are becoming powerful general-purpose tools, but their real value often appears when they are adapted to domain-specific tasks. In this talk, I will present practical strategies for fine-tuning VLMs on specialized data, from dataset design and annotation quality to model selection, evaluation, and deployment constraints. I will discuss common challenges such as limited data, visual ambiguity, domain shift, hallucinations, and maintaining robustness in production. The session will focus on applied lessons for building reliable VLM systems that go beyond demos and solve concrete business or research problems.

About the Speaker

Amine Belhakimi is a Staff AI Engineer at GoPro with over 5 years of experience in computer vision and applied machine learning systems. His work focuses on bringing AI models from research to production, with a growing specialization in vision-language models, multimodal AI, and domain-specific AI applications.

Computer Vision at Nanoscale - Detecting, Segmenting and Analyzing Nanoparticles in microscopic images

This talk covers detecting, segmenting, and analyzing nanoparticles in microscopic images — including YOLO models, Classical CV, and Gradio.

About the Speaker

Atif Anwer is a computer vision and AI researcher with a passion for building intelligent systems that bridge robotics, deep learning, and real-world applications. He earned a joint Ph.D. from INSA Rouen Normandie and Universiti Teknologi PETRONAS, where his work focused on generative AI and image enhancement using polarimetric imaging data.

Towards a Resolution- and Modality-Agnostic Transformers for Earth Observation

Vision Transformers (ViT) dominate computer vision. However, their reliance on rigid patch projectors hinders transfer to Earth Observation (EO), where inputs vary widely in modality, scale, and resolution. We introduce UniverSat, a ViT-style backbone built around a Universal Patch Encoder that maps patches from arbitrary spatial, spectral, and temporal resolutions, and from both optical and non-optical sensors, into a shared embedding space with a single set of weights. This enables training one model on heterogeneous multimodal corpora in self-supervision, yielding robust sensor-agnostic spatial features.

About the Speaker

Guillaume Astruc is pursuing a PhD at Imagine Institute from École des Ponts ParisTech (ENPC), Lastig laboratory at National Institute of Geographic and Forest Information (IGN), and French National Centre for Space Studies (CNES).

Building Real-World Computer Vision Systems with Voxel51

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.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Efficient Image Generation through Smarter Data, Objectives, and Alignment

State-of-the-art image generation models require massive web scrapes and expensive post-training alignment. This talk explores three recent works that challenge the "bigger is better" paradigm to build efficient and controllable models. First, we show how ImageNet alone (only 1/1000th the training data of Stable Diffusion) can match billion-scale models using a fraction of the compute. Second, we introduce a frequency-balanced training objective that overcomes spectral bias, learning high-fidelity textures up to 40% faster. Finally, we present MIRO, a multi-reward pretraining method that bakes human preferences directly into the model, bypassing costly post-hoc RLHF and outperforming models 30x its size.

About the Speaker

Lucas Degeorge is a PhD Student at Ecole Polytechnique and Ecole des Ponts. His main research interest lies in multi-modal generative models.