Search Results for author:
Found 3 papers, 1 papers with code
ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation
Recently, foundational models such as CLIP and SAM have shown promising performance for the task of Zero-Shot Anomaly Segmentation (ZSAS).
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers
We first validate eight manually-defined partial fine-tuning strategies across kinds of datasets and vision transformer architectures, and find that some partial fine-tuning strategies (e. g., ffn only or attention only) can achieve better performance with fewer tuned parameters than full fine-tuning, and selecting appropriate layers is critical to partial fine-tuning.
Efficient Architecture Search via Bi-level Data Pruning
Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant task that has received much attention.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
