With social media usage growing exponentially in the past few years, fake
news has also become extremely prevalent. The detrimental impact of fake news
emphasizes the need for research focused on automating the detection of false
information and verifying its accuracy. In this work, we present the outcome of
the Factify 2 shared task, which provides a multi-modal fact verification and
satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data
calls for a comparison based approach to the task by pairing social media
claims with supporting documents, with both text and image, divided into 5
classes based on multi-modal relations. In the second iteration of this task we
had over 60 participants and 9 final test-set submissions. The best
performances came from the use of DeBERTa for text and Swinv2 and CLIP for
image. The highest F1 score averaged for all five classes was 81.82%.

社交媒体的迅速增长导致虚假新闻泛滥，因此自动检测和验证虚假信息的研究变得尤为重要。本研究介绍了 Factify 2 任务的结果，提供了一个多模态事实验证和讽刺新闻数据集，并使用基于多模态关系将社交媒体索求与支持文件进行比较，最终得到 81.82% 的最高 F1 分数。

Factify 2: 多模态假新闻检测研究结果

Findings of Factify 2: Multimodal Fake News Detection

This paper presents SimMIM, a simple framework for masked image modeling. We
simplify recently proposed related approaches without special designs such as
block-wise masking and tokenization via discrete VAE or clustering. To study
what let the masked image modeling task learn good representations, we
systematically study the major components in our framework, and find that
simple designs of each component have revealed very strong representation
learning performance: 1) random masking of the input image with a moderately
large masked patch size (e.g., 32) makes a strong pre-text task; 2) predicting
raw pixels of RGB values by direct regression performs no worse than the patch
classification approaches with complex designs; 3) the prediction head can be
as light as a linear layer, with no worse performance than heavier ones. Using
ViT-B, our approach achieves 83.8% top-1 fine-tuning accuracy on ImageNet-1K by
pre-training also on this dataset, surpassing previous best approach by +0.6%.
When applied on a larger model of about 650 million parameters, SwinV2-H, it
achieves 87.1% top-1 accuracy on ImageNet-1K using only ImageNet-1K data. We
also leverage this approach to facilitate the training of a 3B model
(SwinV2-G), that by $40\times$ less data than that in previous practice, we
achieve the state-of-the-art on four representative vision benchmarks. The code
and models will be publicly available at this https URL

本文提出了 SimMIM 框架，通过探究每一个组件的简单设计方案，发现简单设计方案表现强劲，并在 ImageNet-1K 数据集上，使用 ViT-B 预训练和微调达到了 83.8％的 top-1 精度，并使用此方法促进 3B 模型（SwinV2-G）的训练，在四个代表性的视觉基准数据集中实现了最新的技术成果。