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
#计算机科学#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 r...
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
#自然语言处理#This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
#计算机科学#PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"
#计算机科学#A lightweight, scalable, and general framework for visual question answering research
#计算机科学#MathVista: data, code, and evaluation for Mathematical Reasoning in Visual Contexts
Implementation of CVPR 2023 paper "Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering".
#人脸识别#a collection of computer vision projects&tools. 计算机视觉方向项目和工具集合。
Strong baseline for visual question answering
[AAAI 2024] NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario.
#大语言模型#[NeurIPS 2024] This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17