在解码器部分:输入调制之后的Token,首先进行时序上的attention,然后再进行空间上的attention,最后是一个FFN,到此为止都是比较常规的Transformer操作,但是本文使用的Decoder并没有使用普通的LN,而是用AdaLN来代替,这个方法主要是在每一个通道内都自适应地估计一个scale参数和一个shift参数,而不是像LN一样对每个通道都进...
论文地址:[2407.05679] BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space (arxiv.org) Abstract 世界模型因其对未来潜在场景的预测能力而在自动驾驶领域受到越来越多的关注。在本文中,我们提出了BEVWorld,这是一种新方法,将多模态传感器输入标记到一个统一且紧凑的鸟瞰图(BEV...
Trained on this dataset, our chat model can engage in free multimodal conversations, where multimodal data can be inserted at will.AnyGPT proposes a generative training scheme that converts all modal data into a unified discrete representation, using the Next Token Prediction task for unified ...
During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal infor
inTab. 15. Then, we thus choseT5-770M, a small but common language model, as our final backbone, because many previous vision-language multimodal works, likeUnified-IOandBLIP, have chosen T5, this encoder-decoder architecture. It shows a strong power to address multi-modal tasks. In ...
Finally, we train the whole model with vision-language pre-training. 3.2 Mixture-of-Modality-Experts Transformer Inspired by mixture-of-experts networks [40, 13], we propose a general-purpose multimodal Transformer for vision-language tasks, namely MoME Transformer, to encode different modalities....
Multimodal instructionLLaVA dataset, Flickr30k, Multi-task conversation✗✗✓ Langauge datasetUnnatural Instructions✗✗✓ Table 2:The training datasets used for our model three-stage training. Stage 1: Pretraining.To have broad vision-language knowledge, our model is trained on a mix of...
We first train the proposed model by the word-level cross-entropy loss(XE). Following the common practice in [8], our model predicts the next token according to the previous ground-truth words which are not the predicted words. The cross-entropy loss 𝐿𝑋𝐸LXE is defined as: 𝐿𝑋...
Extensible MultiModal Annotation Other Markup Languages application/mads+xml .mads Metadata Authority Description Schema Other Markup Languages application/marcxml+xml .mrcx MARC21 XML Schema Other Markup Languages application/mathml+xml .mathml Mathematical Markup Language Other Markup Languages application/...
实验结果表明,TokenFlow 在多模态理解和图像生成任务上都取得了state-of-the-art的性能。 图1:TokenFlow 在多个多模态理解基准测试中的结果。 TokenFlow 在平均得分上带来了显著的提升,证明了其在多模态理解任务中的有效性。 7. 结论: TokenFlow 提出了一个统一的图像分词器,可以有效地桥接多模态理解和生成任务之...