Advancements in language foundation models have primarily fueled the recent
surge in artificial intelligence. In contrast, generative learning of
non-textual modalities, especially videos, significantly trails behind language
modeling. This thesis chronicles our endeavor to build multi-task models for
generating videos and other modalities under diverse conditions, as well as for
understanding and compression applications. Given the high dimensionality of
visual data, we pursue concise and accurate latent representations. Our
video-native spatial-temporal tokenizers preserve high fidelity. We unveil a
novel approach to mapping bidirectionally between visual observation and
interpretable lexical terms. Furthermore, our scalable visual token
representation proves beneficial across generation, compression, and
understanding tasks. This achievement marks the first instances of language
models surpassing diffusion models in visual synthesis and a video tokenizer
outperforming industry-standard codecs. Within these multi-modal latent spaces,
we study the design of multi-task generative models. Our masked multi-task
transformer excels at the quality, efficiency, and flexibility of video
generation. We enable a frozen language model, trained solely on text, to
generate visual content. Finally, we build a scalable generative multi-modal
transformer trained from scratch, enabling the generation of videos containing
high-fidelity motion with the corresponding audio given diverse conditions.
Throughout the course, we have shown the effectiveness of integrating multiple
tasks, crafting high-fidelity latent representation, and generating multiple
modalities. This work suggests intriguing potential for future exploration in
generating non-textual data and enabling real-time, interactive experiences
across various media forms.

通过构建多任务模型、保留高保真度的视频本机时空分词器、以及可解释的词汇术语与视觉观察之间的映射，我们的研究证明了多模态潜在空间设计的可行性，并提出了一种优于行业标准编解码器的视频本机时空分词器，从而在视频合成方面实现了语言模型超越扩散模型的首次突破。进一步地，我们的可扩展视觉令牌表示法在生成、压缩和理解任务中都表现出明显的优势。这项研究的成果为未来在生成非文本数据、实现实时互动体验等方面提供了有趣的潜力。

面向多任务多模态模型的视频生成视角

Towards Multi-Task Multi-Modal Models: A Video Generative Perspective

In this paper, we propose a novel pipeline that leverages language foundation
models for temporal sequential pattern mining, such as for human mobility
forecasting tasks. For example, in the task of predicting Place-of-Interest
(POI) customer flows, typically the number of visits is extracted from
historical logs, and only the numerical data are used to predict visitor flows.
In this research, we perform the forecasting task directly on the natural
language input that includes all kinds of information such as numerical values
and contextual semantic information. Specific prompts are introduced to
transform numerical temporal sequences into sentences so that existing language
models can be directly applied. We design an AuxMobLCast pipeline for
predicting the number of visitors in each POI, integrating an auxiliary POI
category classification task with the encoder-decoder architecture. This
research provides empirical evidence of the effectiveness of the proposed
AuxMobLCast pipeline to discover sequential patterns in mobility forecasting
tasks. The results, evaluated on three real-world datasets, demonstrate that
pre-trained language foundation models also have good performance in
forecasting temporal sequences. This study could provide visionary insights and
lead to new research directions for predicting human mobility.

本文提出了一种新的基于语言基础模型的时间序列模式挖掘流程，以用于人类移动预测任务，该模型通过引入特定提示将数值时间序列转换为句子，以便直接应用于现有语言模型，设计了一个 AuxMobLCast 流程用于预测每个 POI 的访客数量，结合了辅助 POI 分类和编码器 - 解码器架构，实证证明了该流程在移动性预测任务中发现序列模式的有效性，并在三个真实数据集上进行了评估，表现良好。