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
Building blocks for uncertainty-aware cycle consistency presented at NeurIPS'21.

UncertaintyAwareCycleConsistency This repository provides the building blocks and the API for the work presented in the NeurIPS'21 paper Robustness vi

EML Tübingen 19 Dec 12, 2022
Open-source implementation of Google Vizier for hyper parameters tuning

Advisor Introduction Advisor is the hyper parameters tuning system for black box optimization. It is the open-source implementation of Google Vizier w

tobe 1.5k Jan 04, 2023
Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features

MediumVC MediumVC is an utterance-level method towards any-to-any VC. Before that, we propose SingleVC to perform A2O tasks(Xi → Ŷi) , Xi means utter

谷下雨 47 Dec 25, 2022
Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.

Industrial KNN-based Anomaly Detection ⭐ Now has streamlit support! ⭐ Run $ streamlit run streamlit_app.py This repo aims to reproduce the results of

aventau 102 Dec 26, 2022
OBG-FCN - implementation of 'Object Boundary Guided Semantic Segmentation'

OBG-FCN This repository is to reproduce the implementation of 'Object Boundary Guided Semantic Segmentation' in http://arxiv.org/abs/1603.09742 Object

Jiu XU 3 Mar 11, 2019
An open source implementation of CLIP.

OpenCLIP Welcome to an open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training). The goal of this repository is to enable

2.7k Dec 31, 2022
DvD-TD3: Diversity via Determinants for TD3 version

DvD-TD3: Diversity via Determinants for TD3 version The implementation of paper Effective Diversity in Population Based Reinforcement Learning. Instal

3 Feb 11, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
《Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement》(ECCV 2020) GitHub: [fig9]

Unsupervised 3D Human Pose Representation [Paper] The implementation of our paper Unsupervised 3D Human Pose Representation with Viewpoint and Pose Di

42 Nov 24, 2022
GrabGpu_py: a scripts for grab gpu when gpu is free

GrabGpu_py a scripts for grab gpu when gpu is free. WaitCondition: gpu_memory

tianyuluan 3 Jun 18, 2022
PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)

PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition. Transformer models are good at capturing content-based

Soohwan Kim 565 Jan 04, 2023
Understanding Convolution for Semantic Segmentation

TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under

TuSimple 585 Dec 31, 2022
A free, multiplatform SDK for real-time facial motion capture using blendshapes, and rigid head pose in 3D space from any RGB camera, photo, or video.

mocap4face by Facemoji mocap4face by Facemoji is a free, multiplatform SDK for real-time facial motion capture based on Facial Action Coding System or

Facemoji 591 Dec 27, 2022
Differentiable Wavetable Synthesis

Differentiable Wavetable Synthesis

4 Feb 11, 2022
Simple codebase for flexible neural net training

neural-modular Simple codebase for flexible neural net training. Allows for seamless exchange of models, dataset, and optimizers. Uses hydra for confi

Jannik Kossen 7 Apr 05, 2022
Simulation environments for the CrazyFlie quadrotor: Used for Reinforcement Learning and Sim-to-Real Transfer

Phoenix-Drone-Simulation An OpenAI Gym environment based on PyBullet for learning to control the CrazyFlie quadrotor: Can be used for Reinforcement Le

Sven Gronauer 8 Dec 07, 2022
CVPR2021 Workshop - HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization.

HDRUNet [Paper Link] HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization By Xiangyu Chen, Yihao Liu, Zhengwen Zhang, Yu Qiao an

XyChen 105 Dec 20, 2022
This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is accepted to ICCV2021.

GMPQ: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation This is the pytorch implementation for the paper: Generalizable Mix

18 Sep 02, 2022
ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D Data

ARKitScenes This repo accompanies the research paper, ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D

Apple 371 Jan 05, 2023
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

Aaron (Yinghao) Li 282 Jan 01, 2023