Supervised Fine-tuning in turn Improves Visual Foundation Models

Supervised Fine-tuning in turn Improves Visual Foundation Models

18 January 2024

Chun Yuan

Ying Shan

Papers citing "Supervised Fine-tuning in turn Improves Visual Foundation Models"

6 / 6 papers shown

Title
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models Zicheng Ma Chuanliu Fan Zhicong Wang Zhenyu Chen Xiaohan Lin Y. Li Shihao Feng Jun Zhang Ziqiang Cao Y. Gao 43 0 0 11 Mar 2025
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models Junnan Li Dongxu Li Silvio Savarese Steven C. H. Hoi VLM MLLM 244 4,186 0 30 Jan 2023
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021
Multitask Prompted Training Enables Zero-Shot Task Generalization Victor Sanh Albert Webson Colin Raffel Stephen H. Bach Lintang Sutawika ... T. Bers Stella Biderman Leo Gao Thomas Wolf Alexander M. Rush LRM 203 1,651 0 15 Oct 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 283 5,723 0 29 Apr 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Chao Jia Yinfei Yang Ye Xia Yi-Ting Chen Zarana Parekh Hieu H. Pham Quoc V. Le Yun-hsuan Sung Zhen Li Tom Duerig VLM CLIP 293 3,683 0 11 Feb 2021