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Found 3 papers, 3 papers with code
High Resolution Image Quality Database
To demonstrate the importance of a high resolution image quality database for training BIQA models to predict mean opinion scores (MOS) of high resolution images accurately, we trained and tested several traditional and deep learning based BIQA methods on different resolution versions of our database.
Harnessing Diffusion Models for Visual Perception with Meta Prompts
Our key insight is to introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception.
Ranked #2 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation
Coupled with a light segmentation head, we achieve the best trade-off between segmentation accuracy and latency on the ARM-based mobile devices on the ADE20K and Cityscapes datasets.
