Towards Training-free Open-world Segmentation via Image Prompt Foundation Models

17 October 2023

Papers citing "Towards Training-free Open-world Segmentation via Image Prompt Foundation Models"

9 / 9 papers shown

Title
Training-Free Personalization via Retrieval and Reasoning on Fingerprints Deepayan Das Davide Talon Yiming Wang Massimiliano Mancini Elisa Ricci VLM LRM 50 0 0 24 Mar 2025
Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang 23 0 0 09 Oct 2024
ASAM: Boosting Segment Anything Model with Adversarial Tuning Bo Li Haoke Xiao Lv Tang 30 9 0 01 May 2024
Segment Anything Model is a Good Teacher for Local Feature Learning Jindong Wang Ran Tao Zach Wood-Doughty Changwei Wang Xingxu Xie Masashi Sugiyama VLM 23 4 0 29 Sep 2023
Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching Yang Liu Muzhi Zhu Hengtao Li Hao Chen Xinlong Wang Chunhua Shen VLM MLLM 88 83 0 22 May 2023
Customized Segment Anything Model for Medical Image Segmentation Kaiwen Zhang Dong Liu MedIm VLM 106 287 0 26 Apr 2023
Unleashing Text-to-Image Diffusion Models for Visual Perception Wenliang Zhao Yongming Rao Zuyan Liu Benlin Liu Jie Zhou Jiwen Lu ObjD VLM MDE 160 217 0 03 Mar 2023
GroupViT: Semantic Segmentation Emerges from Text Supervision Jiarui Xu Shalini De Mello Sifei Liu Wonmin Byeon Thomas Breuel Jan Kautz Xinyu Wang ViT VLM 189 499 0 22 Feb 2022
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li Dongxu Li Caiming Xiong S. Hoi MLLM BDL VLM CLIP 392 4,137 0 28 Jan 2022