Zero-Shot Transfer Image Classification

7 papers with code • 15 benchmarks • 6 datasets

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Most implemented papers

Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

LiT: Zero-Shot Transfer with Locked-image text Tuning

google-research/vision_transformer CVPR 2022

This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

kakaobrain/coyo-dataset 11 Feb 2021

In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset.

AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities

flagai-open/flagai 12 Nov 2022

In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.

007: Democratically Finding The Cause of Packet Drops

behnazak/Vigil-007SourceCode 20 Feb 2018

Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.

Florence: A New Foundation Model for Computer Vision

microsoft/unicl 22 Nov 2021

Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.

CoCa: Contrastive Captioners are Image-Text Foundation Models

lucidrains/CoCa-pytorch 4 May 2022

We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively.