The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face

Overview

GUESS WHO

Main Links: [Github] [App]

Related Links: [CLIP] [Celeba]

The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face. To discover the image, the player must ask questions that can be answered with a binary response, such as "Yes and No". After every question made by the player, the images that don't share the same answer that the winning one are discarded automatically. The answer to the player's questions, and thus, the process of discarding the images will be established by CLIP. When all the images but one have been discarded, the game is over.

The "Guess Who?" game has a handicap when it uses real images, because it is necessary to always ensure that the same criteria are applied when the images are discarded. The original game uses images with characters that present simple and limited features like a short set of different types of hair colors, what makes it very easy to answer true or false when a user asks for a specific hair color. However, with real images it is possible to doubt about if a person is blond haired or brown haired, for example, and it is necessary to apply a method which ensures that the winning image is not discarded by mistake. To solve this problem, CLIP is used to discard the images that do not coincide with the winner image after each prompt. In this way, when the user asks a question, CLIP is used to classify the images in two groups: the set of images that continue because they have the same prediction than the winning image, and the discarded set that has the opposite prediction. The next figure shows the screen that is prompted after calling CLIP on each image in the game board, where the discarded images are highlighted in red and the others in green. CLIP

Select Images

The first step of the game is to select the images to play. The player can press a button to randomly change the used images, which are taken from the CelebA data set. This data set contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age. (see next figure). CLIP

Ask Questions

The game will allow the player to ask the questions in 4 different ways:

1. Default Question

This option consist on select a question from a list. A drop-down list allows the player to select the question to be asked from a group of pre-set questions, taken from the set of binary labels of the Celeba data set. Under the hood, each question is translated into a pair of textual prompts for the CLIP model to allow for the binary classification based on that question. When they are passed to CLIP along with an image, the model responds by giving a greater value to the prompt that is most related to the image. (see next figure). CLIP

2. Write your own prompt

This option is used to allow the player introducing a textual prompt for CLIP with his/her own words. The player text will be then confronted with the neutral prompt, "A picture of a person", and the pair of prompts will be passed to CLIP as in the previous case. (see next figure) CLIP

3. Write your own two prompts

In this case two text input are used to allow the player write two sentences. The player must use two opposite sentences, that is, with an opposite meaning. (see next figure). CLIP

4. Select a winner

This option does not use the CLIP model to make decisions, the player can simply choose one of the images as the winner and if the player hits the winning image, the game is over. (see next figure). CLIP

Punctuation

To motivate the players in finding the winning image with the minimum number of questions, a scoring system is established so that it begins with a certain number of points (100 in the example), and decreases with each asked question. The score is decreased by subtracting the number of remaining images after each question. Furthermore, there are two extra penalties. The first is applied when the player uses the option "Select a winner". This penalty depends on the number of remaining images, so that the fewer images are left, the bigger will be the penalty. Finally, the score is also decreased by two extra points if, after the player makes a question, no image can be discarded.

Acknowledgements

This work has been supported by the company Dimai S.L and next research projects: FightDIS (PID2020-117263GB-100), IBERIFIER (2020-EU-IA-0252:29374659), and the CIVIC project (BBVA Foundation Grants For Scientific Research Teams SARS-CoV-2 and COVID-19).

Owner
Arnau - DIMAI
Arnau - DIMAI
PaRT: Parallel Learning for Robust and Transparent AI

PaRT: Parallel Learning for Robust and Transparent AI This repository contains the code for PaRT, an algorithm for training a base network on multiple

Mahsa 0 May 02, 2022
This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".

A Memory-saving Training Framework for Transformers This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for Trans

Zhuang AI Group 105 Dec 06, 2022
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On

Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On [Project website] [Dataset] [Video] Abstract We propose a new g

71 Dec 24, 2022
Codes for AAAI22 paper "Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum"

Paper For more details, please see our paper Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum which has been accepted a

14 Sep 30, 2022
Implementation of Fast Transformer in Pytorch

Fast Transformer - Pytorch Implementation of Fast Transformer in Pytorch. This only work as an encoder. Yannic video AI Epiphany Install $ pip install

Phil Wang 167 Dec 27, 2022
A embed able annotation tool for end to end cross document co-reference

CoRefi CoRefi is an emebedable web component and stand alone suite for exaughstive Within Document and Cross Document Coreference Anntoation. For a de

PythicCoder 39 Dec 12, 2022
Metadata-Extractor - Metadata Extractor Script can be used to read in exif metadata

Metadata Extractor The exifextract script can be used to read in exif metadata f

1 Feb 16, 2022
Code for the Paper: Alexandra Lindt and Emiel Hoogeboom.

Discrete Denoising Flows This repository contains the code for the experiments presented in the paper Discrete Denoising Flows [1]. To give a short ov

Alexandra Lindt 3 Oct 09, 2022
MIMIC Code Repository: Code shared by the research community for the MIMIC-III database

MIMIC Code Repository The MIMIC Code Repository is intended to be a central hub for sharing, refining, and reusing code used for analysis of the MIMIC

MIT Laboratory for Computational Physiology 1.8k Dec 26, 2022
Graph Transformer Architecture. Source code for

Graph Transformer Architecture Source code for the paper "A Generalization of Transformer Networks to Graphs" by Vijay Prakash Dwivedi and Xavier Bres

NTU Graph Deep Learning Lab 561 Jan 08, 2023
Introduction to AI assignment 1 HCM University of Technology, term 211

Sokoban Bot Introduction to AI assignment 1 HCM University of Technology, term 211 Abstract This is basically a solver for Sokoban game using Breadth-

Quang Minh 4 Dec 12, 2022
🐥A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

PyTorch implementation of OpenAI's Finetuned Transformer Language Model This is a PyTorch implementation of the TensorFlow code provided with OpenAI's

Hugging Face 1.4k Jan 05, 2023
Efficient training of deep recommenders on cloud.

HybridBackend Introduction HybridBackend is a training framework for deep recommenders which bridges the gap between evolving cloud infrastructure and

Alibaba 111 Dec 23, 2022
Federated_learning codes used for the the paper "Evaluation of Federated Learning Aggregation Algorithms" and "A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison"

Federated Distance (FedDist) This is the code accompanying the Percom2021 paper "A Federated Learning Aggregation Algorithm for Pervasive Computing: E

GETALP 8 Jan 03, 2023
PyTorch implementaton of our CVPR 2021 paper "Bridging the Visual Gap: Wide-Range Image Blending"

Bridging the Visual Gap: Wide-Range Image Blending PyTorch implementaton of our CVPR 2021 paper "Bridging the Visual Gap: Wide-Range Image Blending".

Chia-Ni Lu 69 Dec 20, 2022
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

RaftMLP RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality? By Yuki Tatsunami and Masato Taki (Rikkyo University) [arxiv]

Okojo 20 Aug 31, 2022
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Katsuya Hyodo 8 Oct 13, 2022
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Annealed Flow Transport Monte Carlo

Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud

DeepMind 30 Nov 21, 2022
Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Less is More: Pay Less Attention in Vision Transformers Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers. By

73 Jan 01, 2023