Autoregressive Models in Computer Vision A Comprehensive Review

Autoregressive Models in Computer Vision: A Comprehensive Survey

A new surveypaper, a collaborative effort from leading universities and tech companies, provides a definitive overviewof autoregressive models’ impact on computer vision.

The field of computer vision is rapidly evolving, with autoregressive models emerging as powerful generative models capableof producing impressive results in image generation, video synthesis, 3D modeling, and multi-modal applications. However, the breakneck speed of advancements makeskeeping up with the latest research challenging. A newly published survey paper, Autoregressive Models in Vision: A Survey (arXiv:2411.05902), aims to address this challenge, offering acomprehensive and accessible overview of the field. The collaborative effort, involving researchers from prestigious institutions including the University of Hong Kong, Tsinghua University, Princeton University, Duke University, Ohio State University, UNC, Apple, ByteDance, and theHong Kong Polytechnic University, represents a significant contribution to the understanding and advancement of this crucial area.

A Deep Dive into Autoregressive Modeling in Vision

The paper provides a meticulously curated review of approximately 250 relevant publications, encompassing both established and emerging areas within computer vision. This extensive literature reviewis one of the paper’s key strengths, ensuring a broad and up-to-date perspective. The authors go beyond a simple cataloging of research, critically analyzing the strengths and weaknesses of different approaches, identifying key trends, and highlighting promising future directions. The inclusion of emerging fields like 3D medicalimaging and embodied AI further underscores the paper’s comprehensiveness and forward-looking approach.

Key Highlights:

Comprehensive Literature Review: The survey meticulously covers approximately 250 papers, providing a thorough overview of the current state of the art.
Broad Scope: The paper explores autoregressive models across diverse applications, including image generation, video generation, 3D model generation, and multi-modal generation.
Emerging Fields Covered: The survey incorporates discussions of cutting-edge applications in areas such as 3D medical imaging and embodied AI, reflecting the expanding influence of autoregressive models.
Clear Structure and Accessibility: The paper is structured to provide a clear and accessible framework for researchers, regardless of their level of expertise in the field.
Collaboration and Authority: The collaborative nature of the project, involving researchers from leading universities and tech companies, lends significant weight and credibility to the findings.

Beyond the Survey: Impact and Future Directions

This survey is more than just a literature review; it serves as a valuable resource for researchers, developers, and anyone interested in understanding the current state and future potential of autoregressive models in computer vision. By providing a structured and comprehensive overview,the paper facilitates further research and innovation in this rapidly developing field. The authors conclude by suggesting promising avenues for future research, emphasizing the need for continued exploration of efficiency, scalability, and the integration of autoregressive models with other advanced techniques.

The accompanying GitHub repository (https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey) provides further resources and supplementary materials, enhancing the paper’s accessibility and impact. This comprehensive survey represents a significant contribution to the computer vision community and is highly recommended for anyone seeking a deep understanding of this transformative technology.

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