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Found 5 papers, 2 papers with code
MobileSAMv2: Faster Segment Anything to Everything
The efficiency bottleneck of SegEvery with SAM, however, lies in its mask decoder because it needs to first generate numerous masks with redundant grid-search prompts and then perform filtering to obtain the final valid masks.
Single Image Reflection Removal with Reflection Intensity Prior Knowledge
In this paper, we propose a general reflection intensity prior that captures the intensity of the reflection phenomenon and demonstrate its effectiveness.
Segment Anything Meets Universal Adversarial Perturbation
On top of the ablation study to understand various components in our proposed method, we shed light on the roles of positive and negative samples in making the generated UAP effective for attacking SAM.
Internal-External Boundary Attention Fusion for Glass Surface Segmentation
Inspired by prior semantic segmentation approaches with challenging image types such as X-ray or CT scans, we propose separated internal-external boundary attention modules that individually learn and selectively integrate visual characteristics of the inside and outside region of glass surface from a single color image.
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Concretely, we distill the knowledge from the heavy image encoder (ViT-H in the original SAM) to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM.
