Model Development:
- Design and implement vision-language models for process automation.
- Develop algorithms that integrate visual data with NLP to create cohesive models.
- Experiment with advanced architectures and techniques to enhance model performance.
Data Management:
- Collect, preprocess, and annotate large datasets comprising visual and textual information.
- Implement data augmentation and synthetic data generation techniques to bolster model robustness.
- Ensure data quality and consistency across training and evaluation datasets.
Training and Optimization:
- Train models using high-performance computing resources.
- Optimize models for improved efficiency, accuracy, and scalability.
- Apply transfer learning and fine-tuning strategies to tailor models to specific applications.
Evaluation and Validation:
- Develop and implement evaluation metrics and benchmarks.
- Conduct thorough testing and validation across various tasks and datasets.
- Analyze outputs and iterate to enhance model efficacy.
Research and Innovation:
- Keep abreast of the latest developments in vision-language modeling and AI.
- Innovate and propose new methods and technologies for process automation.
- Publish findings and contribute to the academic and industrial AI communities.
Documentation and Reporting:
- Document all aspects of model development, training, and evaluation.
- Prepare detailed reports and presentations for internal stakeholders.
- Maintain comprehensive records of code, experiments, and data management practices
