DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

Last update: Dec 15, 2022

Overview

DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (UNC Chapel Hill)
Paper

Visual Reasoning

Please see ./paintskills for our DETR-based visual reasoning skill evaluation.

Reference

Please cite our paper if you use our dataset in your works:

@article{Cho2022DallEval,
  title         = {DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers},
  author        = {Jaemin Cho and Abhay Zala and Mohit Bansal},
  year          = {2022},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  eprint        = {2202.04053}
}

Owner

Jaemin Cho

GitHub Repository https://arxiv.org/abs/2202.04053

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DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

Related tags

Overview

DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

Visual Reasoning

Reference

Owner

Jaemin Cho

这是一个利用facenet和retinaface实现人脸识别的库，可以进行在线的人脸识别。

Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

The `rtdl` library + The official implementation of the paper

Code for "NeRS: Neural Reflectance Surfaces for Sparse-View 3D Reconstruction in the Wild," in NeurIPS 2021

Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

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Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".

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