Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

Last update: Jan 03, 2023

Related tags

Deep Learning zero-shot-image-to-text

Overview

Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

[Paper] [Colab is coming soon]

Approach

Example

Usage

To run captioning on a single image:

$ python run.py 
--reset_context_delta
--caption_img_path "example_images/captions/COCO_val2014_000000097017.jpg"

To run model on visual arithmetic:

$ python run.py 
--reset_context_delta
--end_factor 1.06
--fusion_factor 0.95
--grad_norm_factor 0.95
--run_type arithmetics
--arithmetics_imgs "example_images/arithmetics/woman2.jpg" "example_images/arithmetics/king2.jpg" "example_images/arithmetics/man2.jpg"
--arithmetics_weights 1 1 -1

To run model on real world knowledge:

$ python run.py
--reset_context_delta --cond_text "Image of" 
--end_factor 1.04 
--caption_img_path "example_images/real_world/simpsons.jpg"

To run model on OCR:

$ python run.py
--reset_context_delta --cond_text "Image of text that says" 
--end_factor 1.04 
--caption_img_path "example_images/OCR/welcome_sign.jpg"

Owner

GitHub Repository

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Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

Related tags

Overview

Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

Approach

Example

Usage

To run captioning on a single image:

To run model on visual arithmetic:

To run model on real world knowledge:

To run model on OCR:

Owner

ImageNet Adversarial Image Evaluation

DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

Conceptual 12M is a dataset containing (image-URL, caption) pairs collected for vision-and-language pre-training.

MoveNet Single Pose on OpenVINO

SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021

Pipeline code for Sequential-GAM(Genome Architecture Mapping).

For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

The implementation of the algorithm in the paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020.

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Scaling Vision with Sparse Mixture of Experts

A custom DeepStack model for detecting 16 human actions.

CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors

CondenseNet: Light weighted CNN for mobile devices

Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

Official repository for "Restormer: Efficient Transformer for High-Resolution Image Restoration". SOTA results for single-image motion deblurring, image deraining, image denoising (synthetic and real data), and dual-pixel defocus deblurring.

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

PyTorch code of paper "LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering"

Self-Supervised Document-to-Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

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