APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations

8 October 2022

Papers citing "APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations"

5 / 5 papers shown

Title
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment Shenghong Dai Shiqi Jiang Yifan Yang Ting Cao Mo Li Suman Banerjee Lili Qiu 49 2 0 25 Jul 2024
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data Sachin Mehta Maxwell Horton Fartash Faghri Mohammad Hossein Sekhavat Mahyar Najibi Mehrdad Farajtabar Oncel Tuzel Mohammad Rastegari VLM CLIP 38 6 0 24 Apr 2024
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding Haoxiang Wang Pavan Kumar Anasosalu Vasu Fartash Faghri Raviteja Vemulapalli Mehrdad Farajtabar Sachin Mehta Mohammad Rastegari Oncel Tuzel Hadi Pouransari VLM 27 67 0 23 Oct 2023
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts Soravit Changpinyo P. Sharma Nan Ding Radu Soricut VLM 275 1,081 0 17 Feb 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 298 3,693 0 11 Feb 2021