PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

Implementation of 🦩 Flamingo, state-of-the-art few-shot visual question answering attention net out of Deepmind, in Pytorch

X-modaler is a versatile and high-performance codebase for cross-modal analytics(e.g., image captioning, video captioning, vision-language pre-training, visual question answering, visual commonsense reasoning, and cross-modal retrieval).

A collection of resources on applications of multi-modal learning in medical imaging.

Bilinear attention networks for visual question answering

Deep Modular Co-Attention Networks for Visual Question Answering

This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"

PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

A lightweight, scalable, and general framework for visual question answering research

Implementation of CVPR 2023 paper "Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering".

MathVista: data, code, and evaluation for Mathematical Reasoning in Visual Contexts

Strong baseline for visual question answering

a collection of computer vision projects&tools. 计算机视觉方向项目和工具集合。

Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

[AAAI 2024] NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario.

[NeurIPS 2022] Zero-Shot Video Question Answering via Frozen Bidirectional Language Models

[NeurIPS 2024] This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"