Open-Set Object Detection: challenging VLM to understand unknown objects & contexts H/F
Role details
Job location
Tech stack
Requirements
Supervised deep learning models have demonstrated significant performances to detect a closed set of known annotated classes seen during training. But, how will they behave when facing up to objects of unknown classes? As their behaviour is uncertain when subject to never-before-seen classes and contexts (e.g. aerial, medical imaging...), we aim to develop robust Open Set Object Detectors (OSOD), able to localise and classify any objects, no matter their classes are known or unknown during training, nor their domain. In specific domains, being able to provide semantic information about the unknown is also paramount and an understudied problem (e.g., characterizing the super-class of an unknown object)., * Study state-of-the-art methods of Open Set Object Detection (OSOD) as well as Visual Language Models (VLM) in the context of Open World containing both known and unknown objects;
- Design an object detector aware of the existence of the unknown, and able to describe the unknown by comparing it to or distinguishing it from what is known via certain characteristics that can be described textually;
- Evaluate the proposed method on recent OSOD benchmarks and compare to the state of the art, Diplôme préparé
Bac+5 - Master of Science
Formation recommandée
Master or Ecole d'ingénieur
Possibilité de poursuite en thèse
Benefits & conditions
Durée du contrat (en mois), * Challenge these methods by applying them to new contexts (e.g. aerial images, medical imaging);
- If relevant, submit your contributions to an international conference or workshop for publication., Students in their 5th year of studies (M2)
- Computer vision skills
- Machine learning skills (deep learning, VLM…)
- Python proficiency in a deep learning framework (especially PyTorch)