4 papers
Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters
Hinako Mitsuoka, Kazuhiro Hotta
Segment Anything Model 2 (SAM2) demonstrated impressive zero-shot capabilities on natural images but faces challenges in biomedical segmentation due to significant domain shifts an…
Combining Boundary Supervision and Segment-Level Regularization for Fine-Grained Action Segmentation
Hinako Mitsuoka, Kazuhiro Hotta
Recent progress in Temporal Action Segmentation (TAS) has increasingly relied on complex architectures, which can hinder practical deployment. We present a lightweight dual-loss tr…
Accuracy Improvement of Cell Image Segmentation Using Feedback Former
Hinako Mitsuoka, Kazuhiro Hotta
Semantic segmentation of microscopy cell images by deep learning is a significant technique. We considered that the Transformers, which have recently outperformed CNNs in image rec…
Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
Sota Kato, Hinako Mitsuoka, Kazuhiro Hotta
There has been a lot of recent research on improving the efficiency of fine-tuning foundation models. In this paper, we propose a novel efficient fine-tuning method that allows the…