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Multimodal

Goal: Connect vision and language with contrastive encoders, then generative VLMs (Flamingo, BLIP-2, LLaVA), and finally joint spaces across many modalities.

Prereqs: Deep Learning. Computer Vision and LLMs help.

Status: done

Work through the steps in order. Bold links open YouTube.

Step Concept YouTube Read
1 Vision-language models What Are Vision Language Models? How AI Sees & Understands Images Lilian Weng — Generalized Visual Language Models
2 Contrastive language-image pretraining Yannic Kilcher — OpenAI CLIP: Connecting Text and Images Radford et al. 2021 — Learning Transferable Visual Models From Natural Language Supervision
3 Visual question answering Visual Question Answering (VQA) Antol et al. 2015 — VQA: Visual Question Answering
4 Few-shot visual language models Flamingo: Visual Language Model for Few-Shot Learning Alayrac et al. 2022 — Flamingo: a Visual Language Model for Few-Shot Learning
5 Bootstrapping VLMs with frozen encoders BLIP-2 Architecture in 3 minutes! Li et al. 2023 — BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
6 Visual instruction tuning AI Bites — LLaVA: the first instruction following multi-modal model Liu et al. 2023 — Visual Instruction Tuning
7 Large multimodal models landscape CVPR 2023 Tutorial — Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4 Li et al. 2023 — Large Multimodal Models: Notes on CVPR 2023 Tutorial
8 Improved multimodal instruction baselines   Liu et al. 2023 — Improved Baselines with Visual Instruction Tuning
9 Sigmoid language-image pretraining Lucas Beyer — Sigmoid Loss for Language Image Pre-Training Zhai et al. 2023 — Sigmoid Loss for Language Image Pre-Training
10 Binding many modalities AI Bites — ImageBind: One Embedding Space To Bind Them All Girdhar et al. 2023 — ImageBind: One Embedding Space To Bind Them All
11 Instruction-aware vision-language models   Dai et al. 2023 — InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

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