# Eurus-Holmes/Awesome-Multimodal-Research

A curated list of Multimodal Related Research.

Repository: https://github.com/Eurus-Holmes/Awesome-Multimodal-Research
Canonical: https://ross.abutalabs.com/products/awesome-multimodal-research
Language: Python
License: MIT
License Family: permissive
Topics: awesome, multimodal-research, multimodal-learning, multimodal
Last push: 2023-08-05T05:44:56+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2590, "days_push": 1124, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1394, forks 147 (observed 2026-08-28T04:04:36.384835+00:00)

## What it is
A curated awesome-list of research papers and resources on multimodal machine learning, reorganized from Paul Liang's multimodal ML reading list. It aggregates papers, news, and links covering vision-language models, multimodal LLMs, and related research.

## Use cases
- find research papers on multimodal machine learning
- get a reading list for vision-language models
- track recent multimodal LLM research like GPT-4 and PaLM-E
- survey the state of multimodal learning research
- find references for a thesis on multimodal AI

## When to choose
- you need a curated starting point for multimodal ML literature
- you want to survey vision-language and multimodal LLM research
- you are a student or researcher building a reading list

## When to avoid
- you need runnable code or a library rather than paper links
- you need up-to-the-minute coverage after 2023
- you want tutorials with hands-on exercises instead of a paper index

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: artificial-intelligence, machine-learning, deep-learning, computer-vision, tutorials
- platform: cross-platform
- tags: awesome-list, multimodal-learning, research-papers, curated-list, vision-language, natural-language-processing

## Member repositories
- Eurus-Holmes/Awesome-Multimodal-Research (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.384835+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:25.354023+00:00, confidence not recorded.
  - readme: https://github.com/Eurus-Holmes/Awesome-Multimodal-Research (fetched 2026-08-28T04:04:36.384835+00:00, sha 8a71e8f41192)
- Data as of 2026-08-30T08:39:29.467469+00:00.