ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2407.04003
  4. Cited By
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners

Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners

4 July 2024
Mushui Liu
Bozheng Li
Yunlong Yu
    VLM
    CLIP
ArXivPDFHTML

Papers citing "Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners"

4 / 4 papers shown
Title
MaPLe: Multi-modal Prompt Learning
MaPLe: Multi-modal Prompt Learning
Muhammad Uzair Khattak
H. Rasheed
Muhammad Maaz
Salman Khan
F. Khan
VPVLM
VLM
206
531
0
06 Oct 2022
Learning to Prompt for Vision-Language Models
Learning to Prompt for Vision-Language Models
Kaiyang Zhou
Jingkang Yang
Chen Change Loy
Ziwei Liu
VPVLM
CLIP
VLM
345
2,271
0
02 Sep 2021
Open-vocabulary Object Detection via Vision and Language Knowledge
  Distillation
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
Xiuye Gu
Nayeon Lee
Weicheng Kuo
Huayu Chen
VLM
ObjD
225
899
0
28 Apr 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy
  Text Supervision
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
304
3,708
0
11 Feb 2021
1