Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Last update: Dec 28, 2022

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Deep Learning Tip-Adapter

Overview

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Pipeline of Tip-Adapter

Tip-Adapter can provide fast convergence and better performance by leveraging cache-model as initialization of adapter.

Contributor

Peng Gao, Renrui Zhang

Acknowledgment

CLIP and CoOp

Owner

peng gao

Young Scientist at Shanghai AI Lab

GitHub Repository

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Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Related tags

Overview

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Pipeline of Tip-Adapter

Contributor

Acknowledgment

Owner

peng gao

Official codebase used to develop Vision Transformer, MLP-Mixer, LiT and more.

Corgis are the cutest creatures; have 30K of them!

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

Rethinking Transformer-based Set Prediction for Object Detection

Collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors

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Top #1 Submission code for the first https://alphamev.ai MEV competition with best AUC (0.9893) and MSE (0.0982).

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This repository contains all source code, pre-trained models related to the paper "An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator"

Object detection on multiple datasets with an automatically learned unified label space.

OpenCVのGrabCut()を利用したセマンティックセグメンテーション向けアノテーションツール(Annotation tool using GrabCut() of OpenCV. It can be used to create datasets for semantic segmentation.)

Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

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