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
An example to implement a new backbone with OpenMMLab framework.

Backbone example on OpenMMLab framework English | 简体中文 Introduction This is an template repo about how to use OpenMMLab framework to develop a new bac

Ma Zerun 22 Dec 29, 2022
Beginner-friendly repository for Hacktober Fest 2021. Start your contribution to open source through baby steps. 💜

Hacktober Fest 2021 🎉 Open source is changing the world – one contribution at a time! 🎉 This repository is made for beginners who are unfamiliar wit

Abhilash M Nair 32 Dec 11, 2022
Sequence modeling benchmarks and temporal convolutional networks

Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN) This repository contains the experiments done in the work An Empirical Evaluati

CMU Locus Lab 3.5k Jan 01, 2023
Implementations of paper Controlling Directions Orthogonal to a Classifier

Classifier Orthogonalization Implementations of paper Controlling Directions Orthogonal to a Classifier , ICLR 2022, Yilun Xu, Hao He, Tianxiao Shen,

Yilun Xu 33 Dec 01, 2022
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

Abdultawwab Safarji 7 Nov 27, 2022
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Manifold-SCA Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning The repo is org

Yuanyuan Yuan 172 Dec 29, 2022
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Microsoft 1.3k Dec 29, 2022
HairCLIP: Design Your Hair by Text and Reference Image

Overview This repository hosts the official PyTorch implementation of the paper: "HairCLIP: Design Your Hair by Text and Reference Image". Our single

322 Jan 06, 2023
Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Ankou Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering t

SoftSec Lab 54 Dec 24, 2022
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

MNE-Python MNE-Python software is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, E

MNE tools for MEG and EEG data analysis 2.1k Dec 28, 2022
FishNet: One Stage to Detect, Segmentation and Pose Estimation

FishNet FishNet: One Stage to Detect, Segmentation and Pose Estimation Introduction In this project, we combine target detection, instance segmentatio

1 Oct 05, 2022
Blind Video Temporal Consistency via Deep Video Prior

deep-video-prior (DVP) Code for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior PyTorch implementation | paper | project web

Chenyang LEI 272 Dec 21, 2022
A Player for Kanye West's Stem Player. Sort of an emulator.

Stem Player Player Stem Player Player Usage Download the latest release here Optional: install ffmpeg, instructions here NOTE: DOES NOT ENABLE DOWNLOA

119 Dec 28, 2022
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

Sayed Hashim 3 Nov 15, 2022
Bounding Wasserstein distance with couplings

BoundWasserstein These scripts reproduce the results of the article Bounding Wasserstein distance with couplings by Niloy Biswas and Lester Mackey. ar

Niloy Biswas 1 Jan 11, 2022
A unet implementation for Image semantic segmentation

Unet-pytorch a unet implementation for Image semantic segmentation 参考网上的Unet做分割的代码,做了一个针对kaggle地盐识别的,请去以下地址获取数据集: https://www.kaggle.com/c/tgs-salt-id

Rabbit 3 Jun 29, 2022
NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

[Official] FINE Samples for Learning with Noisy Labels This repository is the official implementation of "FINE Samples for Learning with Noisy Labels"

mythbuster 27 Dec 23, 2022
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
Just Randoms Cats with python

Random-Cat Just Randoms Cats with python.

OriCode 2 Dec 21, 2021
4th place solution for the SIGIR 2021 challenge.

SIGIR-2021 (Tinkoff.AI) How to start Download train and test data: https://sigir-ecom.github.io/data-task.html Place it under sigir-2021/data/. Run py

Tinkoff.AI 4 Jul 01, 2022