A Survey on Vision Autoregressive Model (2411.08666v2)

Abstract: Autoregressive models have demonstrated great performance in NLP with impressive scalability, adaptability and generalizability. Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision, which perform next-token predictions by representing visual data as visual tokens and enables autoregressive modelling for a wide range of vision tasks, ranging from visual generation and visual understanding to the very recent multimodal generation that unifies visual generation and understanding with a single autoregressive model. This paper provides a systematic review of vision autoregressive models, including the development of a taxonomy of existing methods and highlighting their major contributions, strengths, and limitations, covering various vision tasks such as image generation, video generation, image editing, motion generation, medical image analysis, 3D generation, robotic manipulation, unified multimodal generation, etc. Besides, we investigate and analyze the latest advancements in autoregressive models, including thorough benchmarking and discussion of existing methods across various evaluation datasets. Finally, we outline key challenges and promising directions for future research, offering a roadmap to guide further advancements in vision autoregressive models.

• The paper presents a comprehensive taxonomy of vision autoregressive models, extending methods from NLP to diverse visual tasks.
• It evaluates key approaches like PixelCNN and VQ-VAE, showcasing improved training efficiency and robust performance on benchmarks such as CIFAR-10 and ImageNet.
• The paper highlights challenges like sequential prediction complexity and resource demands, proposing future directions for efficient tokenization and model integration.

An Insightful Overview of "A Survey on Vision Autoregressive Model"

The paper "A Survey on Vision Autoregressive Model" by Kai Jiang and Jiaxing Huang presents a comprehensive examination of the application of autoregressive (AR) models in computer vision, motivated by their successful deployment in NLP. The document endeavors to systematically categorize and analyze the advancements in AR models as they extend beyond language to visual data tokens, exploring their application across an array of vision tasks.

Overview and Taxonomy

The survey categorizes the contributions of AR models in dimensions of image and video generation, image editing, motion generation, and multimodal capabilities, drawing parallels to their scalability and generalizability in NLP. The paper creates a comprehensive taxonomy that includes methodologies like autoregressive diffusion and multimodal autoregressive models, illustrating the models' ability to handle various complexities in visual tasks, from simple image synthesis to complex 3D generation and robotic manipulation.

Numerical Results and Contributions

Within this survey, specific autoregressive methods are scrutinized for their performance across established benchmarks, including CIFAR-10, ImageNet, MS-COCO, and novel video datasets like UCF-101. Development in models such as PixelCNN, VQ-VAE, and their derivatives demonstrate state-of-the-art proficiency in both unconditional and conditional image generation tasks, highlighting the robust adaptability of AR architectures to a variety of datasets and tasks.

For instance, the research highlights the efficiency of methodologies like Parallelized PixelCNN in reducing computational complexity and achieving competitive results through conditionally independent pixel groups, marking significant improvements in training efficiency and performance metrics over existing models.

Challenges and Future Directions

Despite the strides in autoregressive model development, the paper acknowledges ongoing challenges, such as resource-intensive computations due to sequential pixel prediction and the necessity for improved multimodal integration. As the progression towards unified models like Transfusion shows potential, gaps remain in efficiently coupling generation and understanding across modalities.

The paper suggests focusing on enhancing tokenization strategies and exploring architectural innovations that facilitate more efficient training and inference. There is also an emphasis on the potential for scale-wise generation models to surmount computational trade-offs, ensuring high-quality outputs without exhaustive resource consumption.

Practical and Theoretical Implications

The implications of the survey are twofold. Practically, as vision AR models advance, they promise transformative applications in fields ranging from medical imaging, where precision and real-time data generation are essential, to entertainment and immersive technologies needing dynamic, narrative-driven content generation. Theoretically, the pursuit of uncovering scaling laws applicable to visual data parallels the quest for generalized AI, urging continued exploration of AR models' scalability and generalization principles within vision tasks.

Conclusion

The paper successfully constructs a foundational roadmap for the future of AR models in vision, offering a structured lens through which past and current advancements can be considered, and through which future research can be directed. As the fields of NLP and computer vision continue to converge, the interconnectedness and versatility of autoregressive models will likely hold significant promise for the advancement of general-purpose AI systems. This survey serves as a critical touchpoint in guiding the methodology and application of AR models for complex visual data landscapes.