Stage Human-Object Interaction Benchmarking-Saclay H/F - CEA

Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.

Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.

Implanté au coeur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.

Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :

- La conscience des responsabilités
- La coopération
- La curiosité

Context

The interpretation of human interactions in images or videos has significantly improved with the emergence of Large Language Models (LLMs) and Vision-Language Models (VLMs). However, these large models, whether used directly or distilled into specialized models, still have significant limitations, particularly in accurately attributing interactions to the correct person in dense scenes and discriminating actions in the presence of objects.

Evaluation protocols and databases for this task do not always accurately reflect the true capabilities of the methods due to issues such as annotation imprecision or overly rigid semantic metrics.

What do we expect from you?

To address these problems, the internship will focus on the following objectives:
- Conduct a state-of-the-art review of existing databases and analyze their biases (e.g., precision of detection boxes).
- Propose a semi-automatic pipeline for correcting these biases.
- Identify the biases and gaps in the metrics commonly used in the state-of-the-art.
- Propose a new benchmark, addressing various application domains.
- Evaluate the main state-of-the-art approaches on this benchmark.
- Write a publication about this benchmark.

#Cea List

Profile

- Students in their 4th or 5th year of studies (M1, M2 or gap year)
- Computer vision skills
- Machine learning skills (deep learning, perception models, generative AI...)
- Python proficiency in a deep learning framework (especially TensorFlow or PyTorch)