1. Exploring Similarities in Themes and Style across Age Groups in Children’s Artwork using CLIP 2. Generative Fashion and Assisted Clothing Design with DragGAN
Project Plan and Milestones
ANYTHING TO IMAGE - from Culture Perspective
Weekly Project Plan
| Date | Model programming | Web page development | Data set construction | Experimentation and evaluation |
|---|---|---|---|---|
| Week 4 | Identified the type of model to use | |||
| Week 5 | ImageBind Model development | Website first demo/draft | ||
| Week 5 | ImageBind model test
|
Website frontend design | ||
| Week 6 | State Diffusion Model Deployment | Make a data set search plan | ||
| Week 7 | Diffusion Model test | Division of subtopics and data levels | ||
| Week 8 | Dataset Collection in levels and samples manually | |||
| Week 9 | Dataset Collection in levels and samples manually | Wiki Page for the project development | ||
| Week 10 | Dataset Collection in levels and samples manually | Continue web construction | ||
| Week 11 | Website improvement in frontend and backend mix | Search Culture Benchmark and data analysis processes | ||
| Week 12 | Add samples for differents categories for tested | Generation Culture Benchmark, and data analysis
| ||
| Week 13 | Finalize and improve the website
| |||
| Week 14 | Prepare the Wikipedia page and final report
|
Milestone 1
- ImageBind Model Deployment
- Website Demo front end
Milestone 2
- Dataset Collection
- Diffusion Model Deployment
- Wiki Page for the project
- Website Improvement(Back end & front end)
Milestone 3
- Test, evaluation and data Analysis
- Generation Culture Benchmark
- Prepare final wiki page
- Prepare final presentation
Information extra & appendices for final wiki
"ANYTHING TO IMAGE from Culture Perspective"
Objective The Objective of the project is to prove how far can an AI model understand the cultural differences among 2 cultures presented, we will try to run search/generation processes to prove its understanding.
The model:
- The model we used for the project is Anything2image developed by @Adamdad on Github
The data Base
- The databases are created and defined by the team, manually in 4 different areas with more than 300 samples.
Features It uses stable diffusion 2-1-unclip model integrated with InternGPT, it can receive either audio, image or text or a mix as input and search or generate an image.
2 functions:
Generation
- This is a model that can be used to generate and modify images based on text prompts.It is a latent diffusion model that uded a fixed,pretrained d text encoder (OpenClip - Vit/H)
Search Search function enabled with pretrained models that uses AI to identify objects and match results from a given databe
Current State & future plan:
Finished:
ImageBind Model Deployment State Diffusion Model Deployment Website Demo Website Frontend Design
Ongoing (1 - 8 Dec)
Dataset Collection (Deadline December 5) Wiki Page for the project (Deadline December 6) Website Improvement (Deadline December 8)
Future weeks (8 - 15 Dec)
Search Culture Benchmark (Deadline December 10) Generation Culture Benchmark (Deadline December 15)
Topics:
Part 1 ImageBind & Stable Diffusion Introduction
Part 2 Website
Part 3 Experiment Plan for Search Function
Part 4 Experiment Plan for Generation Function
Part 1.-
The Introduction of GenAI Models
Imagebind’s joint embedding space enables novel multimodal capabilities
This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).
Part 2- Website
Creation of website of the code, mis front end with back end to hace a results: https://dataoni.com/fdh-project-frontend-page/
Part 3: Experiment Plan for Search Part & Data Set construction
Disseminate the diverse cultural perspectives of the world
"From Cultural Perspectives": this involve recognizing and accurately depicting cultural symbols, costumes, architecture, and other elements unique to various cultures.
Cultures around the world Civilizations through time
3.2 Dataset
Data set generated manually, with more than 300 samples analyzed between different cultures over time and also that occupy a different geographical space.
Each sample comprises at least 2 of the followeing elementos Audio, Image, Tec=xt.
Instruments:
China:
String instrument: guzheng, pipa, erhu, gaohu, harp , dulcimer Blowing instrument: flute, xiao, suona. Percussion instrument: gongs, drums, and wooden fish.
European:
String instrument: violin, cello, guitar, harp Woodwind instrument: Flute, clarinet, oboe. Brass instruments: trumpet, trombone, horn Keyboard instrument: piano, organ
America:
String instrument: chalapatita, bajo, cuatro. Blowing instrument: Pan Flute, Saxophone. Percussion instrument: Conga drums, marimba.
Clothes:
Historical period: Ancient Clothing: Egyptian, Greek, and Roman clothing. Medieval Costume: Renaissance Clothing: 17th to 19th Century Costumes: 20th Century Clothing:
Geography & culture:
Asian Costumes: African clothing European clothing: Middle Eastern and Arab clothing: Costumes of the Americas:
Paintings:
Oil paintings: Renaissance: Baroque: Rococo: Romanticism: Impressionism: Post-Impressionism:. Modern and contemporary art:
Sketching
Chinesse pictures:
Lanscape Flowers and Birds Characters
Food
Asia America European Middle Eastern
Experiment Plan for Search Part:
Remark: Only Imagebind
Basic Perception Distinguish between different types of items such as food and paintings or musical instruments and clothing
General Culture Perception Distinguish between same type of items but from different areas or times such as Chinese food and Mexico food or Medieval Costume and 20th Century Clothing
Accurate Culture Perception Distinguish between same type of items but from different specific culture such as Renaissance Oil Painting and Impressionism Oil Painting or Chinese New Year and Mexican Day of the Dead
Part 4
Approach to studying AI model’s intelligence
Key questions: How can we measure the intelligence of an LLM that has been trained on an unknown but extremely vast corpus of web-text data?---The Standard Benchmark
This approach is designed to separate true learning from mere memorization, and is backed up by a rich theoretical framework [SSBD14, MRT18].
Definition of intelligence: Someone who:
Reasoning Planning Problem-solving Abstract thinking Understanding complex ideas Fast learning Ability to learn from experience.
4.1 Approach to studying AI model’s intelligence
They propose a different approach which is closer to traditional psychology rather than machine learning, leveraging human creativity and curiosity.
They aim to generate novel and difficult tasks and questions that convincingly demonstrate that GPT-4 goes far beyond memorization, and that it has a deep and flexible understanding of concepts, skills, and domains
Experiment plan:
Clothes: The ability of the models to recognize, differentiate, and accurately generate traditional and culturally specific attire. Paintings: Assessing the models' capability to identify and replicate cultural themes, styles, and historical contexts in paintings. of specific cultural art forms. Food: Evaluating the models' proficiency in identifying and visually representing traditional dishes from various cultures.
Multimodal and Integrative Ability Basic Sound & Text & Image Instruments & Text & Image Provide a sample of an instrument's sound, like a piano piece or violin solo. The text describes the instrument and the style of music played, such as "a piano concerto from the Romantic period". Request the model to generate an image that integrates the sound and text, depicting the instrument and the style of music.
4.3 Evaluation and coherence
Evaluation Recognize and Differentiate: distinguish between different cultural elements within the same category Accuracy in Representation: portray the identified cultural elements in terms of visual appearance, cultural context, and associated symbolism. Maintaining cultural accuracy and sensitivity System Coherence Put the generation result back into the search model