Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification

8 July 2024

Papers citing "Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification"

9 / 9 papers shown

Title
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,412 0 11 Nov 2021
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling Renrui Zhang Rongyao Fang Wei Zhang Peng Gao Kunchang Li Jifeng Dai Yu Qiao Hongsheng Li VLM 184 384 0 06 Nov 2021
Learning to Prompt for Vision-Language Models Kaiyang Zhou Jingkang Yang Chen Change Loy Ziwei Liu VPVLM CLIP VLM 322 2,249 0 02 Sep 2021
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 166 684 0 22 Apr 2021
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval Huaishao Luo Lei Ji Ming Zhong Yang Chen Wen Lei Nan Duan Tianrui Li CLIP VLM 309 778 0 18 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
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,913 0 31 Dec 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,471 0 17 Apr 2017
MatConvNet - Convolutional Neural Networks for MATLAB Andrea Vedaldi Karel Lenc 183 2,947 0 15 Dec 2014