InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective

28 May 2025

Papers citing "InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective"

7 / 7 papers shown

Title
SAMCL: Empowering SAM to Continually Learn from Dynamic Domains Zeqing Wang Kangye Ji Di Wang Fei Cheng VLM 64 1 0 06 Dec 2024
Segment Anything in High Quality Lei Ke Mingqiao Ye Martin Danelljan Yifan Liu Yu-Wing Tai Chi-Keung Tang Feng Yu VLM 69 322 0 02 Jun 2023
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models Elad Ben-Zaken Shauli Ravfogel Yoav Goldberg 116 1,191 0 18 Jun 2021
Anabranch Network for Camouflaged Object Segmentation Trung-Nghia Le Tam V. Nguyen Zhongliang Nie M. Tran Akihiro Sugimoto 58 486 0 20 May 2021
Distilling Knowledge via Knowledge Review Pengguang Chen Shu Liu Hengshuang Zhao Jiaya Jia 163 429 0 19 Apr 2021
CyCADA: Cycle-Consistent Adversarial Domain Adaptation Judy Hoffman Eric Tzeng Taesung Park Jun-Yan Zhu Phillip Isola Kate Saenko Alexei A. Efros Trevor Darrell 93 2,985 0 08 Nov 2017
Deep Learning and the Information Bottleneck Principle Naftali Tishby Noga Zaslavsky DRL 53 1,570 0 09 Mar 2015