3 accepted papers
In this paper we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM known for its zero-shot generalizability exhibits a performance degradation when faced with datasets with varying image sizes. Previous approaches tend to resize the image to a fixed size o…
Self-supervised pre-training has been widely utilized on various vision tasks and gains a great success. However, pre-training on big datasets suffers a lengthy training schedule and large memory consumption. To alleviate these problems, we propose a light-weighted model called Convolutional Lite Ma…
Implicit Neural Representations (INRs) are powerful to parameterize continous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution, and image generation. The questions on how to explore INRs to high-level tasks a…