Computer Vision LaboratoryOpen OpportunitiesThe field of image restoration is continually evolving with the introduction of advanced deep learning models capable of tackling increasingly complex restoration tasks. The use of foundation models, which are pre-trained on diverse data before being fine-tuned for specific tasks, has demonstrated considerable promise in various domains of artificial intelligence. This proposal aims to develop a new foundation model for image restoration by incorporating the state-space model and enhancing it with text prompt capabilities. This approach will allow the model to perform targeted restorations based on descriptive textual prompts, significantly improving the precision and quality of the restoration process. - Computer Vision
- Collaboration, ETH for Development (ETH4D) (ETHZ), Master Thesis
| Open vocabulary video semantic segmentation (OV-VSS) aims to assign a semantic label to each pixel of each frame of the video given an arbitrary set of open-vocabulary category names. There are a number of attempts on open vocabulary image semantic segmentation (OV-ISS). However, OV-VSS does not get enough attention due to the difficulty of video understanding tasks in modeling local redundancy and global correlation. In this master thesis project, we plan to fill the gap by extending existing OV-ISS methods to OV-VSS. Specifically, we aim to develop a OV-VSS method which achieves high accuracy by using temporal information and keeps high efficiency.
- Artificial Intelligence and Signal and Image Processing
- Master Thesis
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