A Comprehensive Evaluation of Foundation Models for Few-Shot Semantic Segmentation
The core message of this article is that the DINO V2 foundation model consistently outperforms other prominent foundation models, such as Segment Anything, CLIP, and Masked AutoEncoder, in the task of few-shot semantic segmentation across various datasets and adaptation methods.
MSDNet: A Transformer-Guided Multi-Scale Decoder for Efficient Few-Shot Semantic Segmentation
A novel framework for few-shot semantic segmentation that leverages a transformer-based spatial decoder, a multi-scale decoder, and global feature integration to achieve state-of-the-art performance with a compact model architecture.
다양한 지원 이미지에 대한 정보를 활용하여 적은 수의 예제로도 효과적으로 세그먼테이션을 수행하는 MSDNet: 트랜스포머 기반 프로토타이핑을 통한 다중 스케일 디코더
제한된 수의 지원 이미지를 활용하여 쿼리 이미지의 세그먼테이션 마스크를 정확하게 예측하는 새로운 프레임워크를 제안한다. 이를 위해 공간 변환 디코더, 다중 스케일 디코더, 그리고 컨텍스트 마스크 생성 모듈을 도입하여 지원 이미지와 쿼리 이미지 간의 관계를 효과적으로 모델링한다.
DiffewS: A Diffusion Model for Few-Shot Semantic Segmentation
This research introduces DiffewS, a novel framework leveraging Latent Diffusion Models (LDMs) for Few-Shot Semantic Segmentation, demonstrating its superior performance and efficiency compared to existing methods, particularly in in-context learning settings.
Graph-Based Few-Shot Semantic Segmentation Using the Segment Anything Model
This research paper introduces a novel, efficient, and training-free approach for few-shot semantic segmentation that leverages the Segment Anything Model (SAM) and graph-based analysis to achieve state-of-the-art results without requiring extensive parameter tuning or training.
ポイントをつなぐ：グラフベースのFew-Shotセマンティックセグメンテーション
本稿では、大規模事前学習済みモデルSAMを用いたFew-Shotセマンティックセグメンテーションにおいて、ポイントプロンプトとマスクの関係性をグラフで表現することで、高精度かつ効率的なセグメンテーションを実現する新しい手法を提案する。
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