Multi-Agent VQA: Exploring Multi-Agent Foundation Models on Zero-Shot Visual Question Answering
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Updated
Jun 10, 2024 - Python
Multi-Agent VQA: Exploring Multi-Agent Foundation Models on Zero-Shot Visual Question Answering
glTF 2.0 SDK for JavaScript and TypeScript, on Web and Node.js.
All projects developed with ThreeJS at Interactive Graphical Systems at FEUP in 2023/2024
[NeurIPS 2023] The repo of CommonScenes, a scene generation method powered by the diffusion model.
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion.
[ICRA 2024] SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs
This is the official implementation of the paper "Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge" in PyTorch.
SSG-VQA is a Visual Question Answering (VQA) dataset on laparoscopic videos providing diverse, geometrically grounded, unbiased and surgical action-oriented queries generated using scene graphs.
Repository for ICRA'24 Paper "Optimal Scene Graph Planning with Large Language Model Guidance"
FACTUAL benchmark dataset, the pre-trained textual scene graph parser trained on FACTUAL.
Official Pytorch Implementation of Pair then Relation: Pair-Net for Panoptic Scene Graph Generation
Benchmarking Panoptic Video Scene Graph Generation (PVSG), CVPR'23
A scalable pipeline tool for graphics applications to build assets.
[CVPR 2024] DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis
Official implementation for "Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training" https://arxiv.org/abs/2211.11138
Light-weight 3D Scene graph library with renderer in WebGL
[CVPR'24] GraphDreamer: a novel framework of generating compositional 3D scenes from scene graphs.
NeuSyRE: A Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph Enrichment
Official code of the paper 4D-OR: Semantic Scene Graphs for OR Domain Modeling accepted at MICCAI 2022. This repo includes both the dataset and our code.
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