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Found 3 papers, 3 papers with code
ViTGaze: Gaze Following with Interaction Features in Vision Transformers
Our method achieves state-of-the-art (SOTA) performance among all single-modality methods (3. 4% improvement on AUC, 5. 1% improvement on AP) and very comparable performance against multi-modality methods with 59% number of parameters less.
Matte Anything: Interactive Natural Image Matting with Segment Anything Models
In our work, we leverage vision foundation models to enhance the performance of natural image matting.
ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers
Recently, plain vision Transformers (ViTs) have shown impressive performance on various computer vision tasks, thanks to their strong modeling capacity and large-scale pretraining.
Ranked #2 on
Image Matting
on Distinctions-646
