Second MetaFood Workshop at CVPR 2025 (Deadline Extension)

March 18th, 2025 Daniela Lopez de Luise
Dear Community,

We are pleased to announce the call for papers for “The Second MetaFood Workshop” at CVPR 2025.

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The MetaFood Workshop is a premier venue for research that addresses the pressing challenges of applying state-of-the-art computer vision algorithms to food-related data. While modern computer vision models excel on well-curated datasets, they struggle when confronted with food data in natural, uncontrolled environments—often called “data in the wild”. This difficulty arises from the inherent complexities of food imagery, such as noisy, watermarked, and low-resolution data commonly found online.

The MetaFood Workshop aims to bridge this gap by showcasing groundbreaking research and fostering discussions on innovative methods for analyzing and digitizing food data. We invite the computer vision community to tackle these challenges and contribute solutions that can drive forward the fields of food computing and AI-driven food analysis.

Our program will feature technical paper presentations and invited talks from leading experts. 

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Topics of interest include, but are not limited to:
  •     Multi-modal food data analysis covering images, video, audio, structured and/or unstructured text data
  •     Reasoning, in-context learning, and retrieval-augmented generation with Large-Language Models (LLMs)/ Vision-Language Models (VLMs) for food
  •     Food ontologies and LLM-based models for food data analysis
  •     Visual question answering with VLMs for food
  •     Food data analysis and uncertainty modeling
  •     Learning with noisy food labels
  •     Continual, self-supervised, semi-supervised, and unsupervised learning for food
  •     Food classification/detection/segmentation with 2D/3D bias
  •     Food 3D model reconstruction, Novel View Synthesis for food objects
  •     Food portion/nutrition value estimation
  •     Food manipulation understanding, robotic perception systems for food manipulation
  •     Food image quality analysis/inspection
  •     Food image/video generation and Generative AI
  •     Food video analysis and action (eating, cooking, etc.) recognition
  •     Fine-grained dietary monitoring and nutrition assessment
We welcome original research that presents novel applications, innovative algorithms, or critical analyses addressing these challenges. Submissions focusing exclusively on algorithm development without application to food data are better suited for the main CVPR conference.

Join us at the MetaFood Workshop to explore how computer vision can revolutionize food understanding and contribute to solving real-world challenges in food computing.
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Important Dates:

  •     Paper submission deadline: 14th March 2025 (11:59 pm AOE)
  •     Supplementary material deadline: 16th March 2025 (11:59 pm AOE)
  •     Notification to authors: 1st April 2025
  •     Camera-ready deadline: 7th April 2025 (11:59 pm AOE)

Website: https://sites.google.com/view/cvpr-metafood-2025/

We look forward to your contributions.

Regards,
Second MetaFood Workshop – Organizing Team

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Call for Competition on text line segmentation: FEST @ ICDAR 2025

March 18th, 2025 Daniela Lopez de Luise
 

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CALL FOR COMPETITION: FEST @ ICDAR 2025

Competition on few-shot text line segmentation

https://ai4ch.uniud.it/FESTcompICDAR25/

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We are glad to announce FEST: Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST), in conjunction with the 19th International Conference on Document Analysis and Recognition ICDAR 2025.

 

Competition Overview:

Text line segmentation is a crucial component of Document Image Analysis, particularly for ancient handwritten documents. It directly influences subsequent tasks like optical character recognition, layout analysis, and information extraction.

This competition challenges participants to develop systems capable of segmenting text lines using only three annotated images per manuscript for training. The dataset features a diverse collection of ancient manuscripts with varying layouts, levels of degradation, and non-standard formatting, reflecting real-world complexities. By focusing on few-shot learning, this competition aims to encourage the development of methods that can be readily applied by humanities scholars without the need for extensive manual annotation, fostering broader adoption of automated document analysis tools.

 

Competition Tasks:

We challenge the participants to perform the task of textline segmentation on the proposed dataset, U-DIADS-TL, in a few-shot setting, providing only three images, with their corresponding ground truths, as the training set for each of the three manuscripts comprising it.

 

Important Dates:

·        Registration Open: 10 December, 2024

·        Test set release to participants: 7 April, 2025

·        Deadline of the Competition: 20 April, 2025

·        Winner announcement: 30 June, 2025

 

 

For any additional information, please visit the website: https://ai4ch.uniud.it/FESTcompICDAR25/

 

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🚀 Nuevo Curso INVERTER – Aprende a Fabricar Probadores y Herramientas 🔧

March 18th, 2025 Daniela Lopez de Luise
Si la tecnología INVERTER es lo tuyo, este curso es para ti

🔹 Curso: Tecnología INVERTER – Fabricación de Probadores y Herramientas

Aprenderás las diferencias entre sistemas tradicionales e inverter, el funcionamiento de motores de 3 y 5 cables, compresores, forzadores y más. Además, con diagramas detallados, descubrirás cómo fabricar probadores y herramientas para diagnosticar y verificar cada componente

📅 Inicio: 25 de marzo (4 clases en vivo)

🔗 ¡Inscríbete ahora presionando aquí!

Si quieres más detalles, haz clic aquí para ver la información completa

No dejes pasar esta oportunidad. ¡Te esperamos en el curso!

Saludos, Mauricio Etcheverry y Equipo de YoReparo

¡Saludos!

Mauricio Etcheverry y el equipo de YoReparo.com

El equipo de YoReparo.com


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2nd CfP: The 4th Benchmark for Autonomous Robot Navigation (BARN) Challenge — ICRA 2025 Competition

March 18th, 2025 Daniela Lopez de Luise
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Lessons Learned from The BARN Challenge 2024: https://cs.gmu.edu/~xiao/papers/barn24_report.pdf 
Lessons Learned from The BARN Challenge 2023: https://cs.gmu.edu/~xiao/papers/barn23_report.pdf
Lessons Learned from The BARN Challenge 2022: https://cs.gmu.edu/~xiao/papers/barn22_report.pdf
Dear roboticists,
are you interested in agile robot navigation in highly constrained spaces with a lot of obstacles around, e.g., cluttered households or after-disaster scenarios? Do you think mobile robot navigation is mostly a solved problem? Are you looking for a hands-on project for your robotics class, but may not have (sufficient) robot platforms for your students?
If your answer is yes to any of the above questions, we sincerely invite you to participate in The 4th BARN Challenge at ICRA 2025 (https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge25.html)! The BARN Challenge aims at evaluating state-of-the-art autonomous navigation systems to move robots through highly constrained environments in a safe and efficient manner. The task is to navigate a standardized Clearpath Jackal robot from a predefined start to a goal location as quickly as possible without any collision. The challenge will take place both in the simulated BARN dataset and in physical obstacle courses at ICRA 2025.
1. The competition task is designing ground navigation systems to navigate through all 300 BARN environments (https://cs.gmu.edu/~xiao/Research/BARN/BARN.html) and physical obstacle courses constructed at ICRA 2025 as fast as possible without collision.
2. The 300 BARN environments can be the training set for learning-based methods, or to design classical approaches in. During the simulation competition, we will generate another 50 unseen environments unavailable to the participants before the competition.
3. We will standardize a Jackal robot in the Gazebo simulation, including a Hokuyo 2D LiDAR, motor controller of 2m/s max speed, etc.
4. Participants can use any approaches to tackle the navigation problem, such as using classical sampling-based or optimization-based planners, end-to-end learning, or hybrid approaches. We will provide baselines for reference. 
5. A standardized scoring system is provided on the website.
6. We will invite the top teams in simulation to compete in the real world. The team who achieves the fastest collision-free navigation in the physical obstacle courses wins.
If you are interested in participating, please submit your navigation system at https://docs.google.com/forms/d/e/1FAIpQLSfYFw_BVg-7lpXNVxQIX_xQKpFrU2m4jrXAVvrRv8pLzgLkLw/viewform 
Co-Organizers:
Xuesu Xiao (George Mason University)
Zifan Xu (UT Austin)
Aniket Datar (George Mason University)
Saad Abdul Ghani (George Mason University)
Peter Stone (UT Austin / Sony AI)
Sponsor:
Clearpath Robotics, https://clearpathrobotics.com/
Thanks
Xuesu
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CF PE-WASUN 2025

March 18th, 2025 Daniela Lopez de Luise
Design by 2b Consult