What we’re about
đź–– This virtual group is for data scientists, machine learning engineers, and open source enthusiasts.
Every month we’ll bring you diverse speakers working at the cutting edge of AI, data science, machine learning, and computer vision.
- Are you interested in speaking at a future Meetup?
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Contact the Meetup organizers!
This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset. To learn more about FiftyOne, visit the project page on GitHub.
Upcoming events (4+)
See all- Network event85 attendees from 16 groups hostingECCV Redux: Day 1 - Nov 19Link visible for attendees
Missed the European Conference on Computer Vision (ECCV) last month? Have no fear, we have collected some of the best research from the show into a series of online events.
Fast and Photo-realistic Novel View Synthesis from Sparse Images
Novel view synthesis generates new perspectives of a scene from a set of 2D images, enabling 3D applications like VR/AR, robotics, and autonomous driving. Current state-of-the-art methods produce high-fidelity results but require a lot of images, while sparse-view approaches often suffer from artifacts or slow inference. In this talk, I will present my research work focused on developing fast and photorealistic novel view synthesis techniques capable of handling extremely sparse input views.
ECCV 2024 Paper: CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians
About the Speaker
Avinash Paliwal is a PhD Candidate in the Aggie Graphics Group at Texas A&M University. His research is focused on 3D Computer Vision and Computational Photography.
Robust Calibration of Large Vision-Language Adapters
We empirically demonstrate that popular CLIP adaptation approaches, such as Adapters, Prompt Learning, and Test-Time Adaptation, substantially degrade the calibration capabilities of the zero-shot baseline in the presence of distributional drift. We identify the increase in logit ranges as the underlying cause of miscalibration of CLIP adaptation methods, contrasting with previous work on calibrating fully-supervised models. Motivated by these observations, we present a simple and model-agnostic solution to mitigate miscalibration, by scaling the logit range of each sample to its zero-shot prediction logits
ECCV 2024 Paper: Robust Calibration of Large Vision-Language Adapters
About the Speaker
Balamurali Murugesan is currently pursuing his Ph.D. in developing reliable deep learning models. Earlier, he completed his master’s thesis on accelerating MRI reconstruction. He has published 25+ research articles in renowned venues.
Tree-of-Life Meets AI: Knowledge-guided Generative Models for Understanding Species Evolution
A central challenge in biology is understanding how organisms evolve and adapt to their environment, acquiring variations in observable traits across the tree of life. However, measuring these traits is often subjective and labor-intensive, making trait discovery a highly label-scarce problem. With the advent of large-scale biological image repositories and advances in generative modeling, there is now an opportunity to accelerate the discovery of evolutionary traits. This talk focuses on using generative models to visualize evolutionary changes directly from images without relying on trait labels.
ECCV 2024 Paper: Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
About the Speaker
Mridul Khurana is a PhD student at Virginia Tech and a researcher with the NSF Imageomics Institute. His research focuses on AI4Science, leveraging multimodal generative modeling to drive discoveries across scientific domains.
- Network event33 attendees from 16 groups hostingECCV Redux: Day 3 - Nov 21Link visible for attendees
Missed the European Conference on Computer Vision (ECCV) last month? Have no fear, we have collected some of the best research from the show into a series of online events.
Closing the Gap Between Satellite and Street-View Imagery Using Generative Models
With the growing availability of satellite imagery (e.g., Google Earth), nearly every part of the world can be mapped, though street-view images remain limited. Creating street views from satellite data is crucial for applications like virtual model generation, media content enhancement, 3D gaming, and simulations. This task, known as satellite-to-ground cross-view synthesis, is tackled by our geometry-aware framework, which maintains geometric precision and relative geographical positioning using satellite information.
ECCV 2024 Paper
About the Speaker
Ningli Xu is a Ph.D. student at The Ohio State University, specializing in generative AI and computer vision, with a focus on addressing image and video generation challenges in the geospatial domain.
High-Efficiency 3D Scene Compression Using Self-Organizing Gaussians
In just over a year, 3D Gaussian Splatting (3DGS) has made waves in computer vision for its remarkable speed, simplicity, and visual quality. Yet, even scenes of a single room can exceed a gigabyte in size, making it difficult to scale up to larger environments, like city blocks. In this talk, we’ll explore compression techniques to reduce the 3DGS memory footprint. We’ll dive deeply into our novel approach, Self-Organizing Gaussians, which proposes to map splatting attributes into a 2D grid, using a high-performance parallel linear assignment sorting developed to reorganize the splats on the fly. This grid assignment allows us to leverage traditional 2D image compression techniques like JPEG to efficiently store 3D data. Our method is quick and easy to decompress and provides a surprisingly competitive compression ratio. The drastically reduced memory requirements make this method perfect for efficiently streaming 3D scenes at large scales, which is especially useful for AR, VR and gaming applications.
ECCV 2024 Paper
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
About the Speaker
Wieland Morgenstern is a Research Associate at the Computer Vision & Graphics group at Fraunhofer HHI and is pursuing a PhD at Humboldt University Berlin. His research focuses on representing 3D scenes and virtual humans.
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
We present Skeleton Recall Loss, a novel loss function for topologically accurate and efficient segmentation of thin, tubular structures, such as roads, nerves, or vessels. By circumventing expensive GPU-based operations, we reduce computational overheads by up to 90% compared to the current state-of-the-art, while achieving overall superior performance in segmentation accuracy and connectivity preservation. Additionally, it is the first multi-class capable loss function for thin structure segmentation.
ECCV 2024 Paper
About the Speakers
Maximilian Rokuss holds a M.Sc. in Physics from Heidelberg University, now PhD Student in Medical Image Computing at German Cancer Research Center (DKFZ) and Heidelberg University
Yannick Kirchoff holds a M.Sc. in Physics from Heidelberg University, now PhD Student in Medical Image Computing at German Cancer Research Center (DKFZ) and Helmholtz Information and Data Science School for Health
- Nov 22 - Berlin AI, Machine Learning and Computer Vision MeetupMotionLab.Berlin, Berlin
Register to reserve your spot!
Date and Time
Nov 22, 2024 from 5:30 PM to 8:30 PM
Location
The Meetup will take place at MotionLab.Berlin, Bouchéstraße 12/Halle 20 in Berlin
When the Medium is the Message: Addressing Input Biases in Multimodal/Multilingual Models
An embedding model is trained to produce outputs that ensure that semantic similarity is preserved as distance in embedding spaces — like is near like and far from unlike. But models trained with diverse kinds of inputs, i.e. different media and different languages, learn to treat those properties as semantic properties. Two pictures are more “semantically alike” than a picture and a descriptive text that matches it. Similar problems arise with multilingual models: Two English sentences are more alike than an English sentence and a Chinese translation. This undermines the general utility of embedding models. This presentation shows evidence of where this comes from and offers approaches to mitigate the problem.
About the Speaker
Scott Martens is a long-term veteran of AI and NLP research, having started working at AI start-ups in 1994, and a KU Leuven graduate with a doctorate in linguistics. His background includes machine translation development and the intersection between linguistics, philology, and modern AI. Dr. Martens is a Senior Content Manager and Evangelist at Jina AI in Berlin.
Vector Streaming: The Memory Efficient Indexing for Vector Databases
Vector databases are everywhere, powering LLMs. Indexing vectors, especially multivector embeddings like ColPali and Colbert, at a bulk is memory intensive. Vector streaming solves this problem by parallelizing the tasks of parsing, chunking, and embedding generation and indexing it continuously chunk by chunk instead of bulk. This not only increase the speed but also makes the whole task more optimized and memory efficient. Supports, Weaviate, Elastic and Pinecone.
About the Speaker
Sonam Pankaj is a GenerativeAI Evangelist at Articul8-ai and the co-creator and maintainer of the open-source library called Embed-Anything, which helps to create local dense, splade, and multimodal embeddings and index them to vector databases; it’s built-in Rust for speed and efficiency . She worked previously at Qdrant Engine and Rasa. Previously, she also worked as an AI researcher at Saama and has worked extensively on clinical trial analytics. She is passionate about topics like metric learning and biases in language models. She has also published a paper in the most reputed journal of computational linguistics, COLING, in ACL Anthology.
How to Unlock More Value from Self-Driving Datasets
AV/ADAS is one of the most advanced fields in Visual AI. However, getting your hands on a high quality dataset can be tough, let alone working with them to get a model to production. In this talk, I will show you the leading methods and tools to help visualize as well take these datasets to the next level. I will demonstrate how to clean and curate AV datasets as well as perform state of the art augmentations using diffusion models to create synthetic data that can empower the self driving car models of the future,
About the Speaker
Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.
- Network event22 attendees from 16 groups hostingECCV Redux: Day 4 - Nov 22Link visible for attendees
Missed the European Conference on Computer Vision (ECCV) last month? Have no fear, we have collected some of the best research from the show into a series of online events.
Zero-shot Video Anomaly Detection: Leveraging Large Language Models for Rule-Based Reasoning
Video Anomaly Detection (VAD) is critical for applications such as surveillance and autonomous driving. However, existing methods lack transparent reasoning, limiting public trust in real-world deployments. We introduce a rule-based reasoning framework that leverages Large Language Models (LLMs) to induce detection rules from few-shot normal samples and apply them to identify anomalies, incorporating strategies such as rule aggregation and perception smoothing to enhance robustness. The abstract nature of language enables rapid adaptation to diverse VAD scenarios, ensuring flexibility and broad applicability.
ECCV 2024 Paper
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
About the Speaker
Yuchen Yang is a a Ph.D. Candidate in the Department of Computer Science at Johns Hopkins University. Her research aims to deliver functional, trustworthy solutions for machine learning and AI systems.
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models
In this talk, I will introduce our recent work on open-vocabulary 3D semantic understanding. We propose a novel method, namely Diff2Scene, which leverages frozen representations from text-image generative models, for open-vocabulary 3D semantic segmentation and visual grounding tasks. Diff2Scene gets rid of any labeled 3D data and effectively identifies objects, appearances, locations and their compositions in 3D scenes.
ECCV 2024 Paper
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models
About the Speaker
Xiaoyu Zhu is a Ph.D. student at Language Technologies Institute, School of Computer Science, Carnegie Mellon University. Her research interest is computer vision, multimodal learning, and generative models.
