Enhancing Visual Reasoning in Vision-Language Models: A Research Opportunity
il y a 2 semaines
At CEA, we are committed to advancing the field of computer vision and artificial intelligence. We are seeking a highly motivated research intern to join our team and contribute to the development of vision-language models.
Our research focuses on enhancing the visual reasoning capabilities of vision-language models through dynamic visual feature selection. This involves leveraging the Chain-of-Thought (CoT) technique to iteratively select the most relevant visual features during the text generation process.
The successful candidate will have the opportunity to work on a cutting-edge project that aims to tackle limitations in vision-language models, particularly those trained through instruction learning methods like LLAVA. The project involves converting visual features from a Visual Transformer model into text embedding space before feeding them to a large language model (LLM) for text generation.
We are looking for a student with a strong background in computer vision and machine learning, as well as experience with deep learning frameworks such as TensorFlow or PyTorch. The ideal candidate will have a keen interest in research and development, and be able to work independently as well as part of a team.
Requirements:
- Students in their 5th year of studies (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)
- Scientific research experience will be appreciated
Location: Saclay, France
CEA's commitment to diversity and inclusion: We are an equal opportunities employer and welcome applications from all qualified candidates, regardless of their background or disability.
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