SPAct: Self-supervised Privacy Preservation for Action Recognition (2203.15205v1)

Abstract: Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames of video dataset for privacy labels is not feasible. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. For the first time, we present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels. Our training framework consists of three main components: anonymization function, self-supervised privacy removal branch, and action recognition branch. We train our framework using a minimax optimization strategy to minimize the action recognition cost function and maximize the privacy cost function through a contrastive self-supervised loss. Employing existing protocols of known-action and privacy attributes, our framework achieves a competitive action-privacy trade-off to the existing state-of-the-art supervised methods. In addition, we introduce a new protocol to evaluate the generalization of learned the anonymization function to novel-action and privacy attributes and show that our self-supervised framework outperforms existing supervised methods. Code available at: https://github.com/DAVEISHAN/SPAct

• The paper provides a thorough analysis of CVPR LaTeX author guidelines to standardize manuscript preparation and enforce IEEE standards.
• It details language requirements, structured formatting, and precise layout rules that support consistency during submission and review.
• The guidelines emphasize proper anonymization and content length control to facilitate a streamlined and objective peer-review process.

Analysis of LaTeX Author Guidelines for CVPR Proceedings

The document under consideration provides comprehensive guidelines for authors preparing submissions for the CVPR (Conference on Computer Vision and Pattern Recognition) proceedings. This paper serves as a technical manuscript that delineates a structured approach to formatting and organizing research papers in compliance with IEEE standards for publication. The guidelines cover various aspects of manuscript preparation, from language requirements to document structuring and stylistic elements important for maintaining consistency across submissions.

Key Aspects of the Document

Language and Content Requirements:

Firstly, the document mandates that all manuscripts must be written in English, which aligns with the conventions of academic publishing in the field of computer science. Additionally, it discusses the legitimacy and expectations regarding dual submissions. Authors must ensure that their submissions are original and should abide by CVPR’s policies against simultaneous submissions to multiple venues without explicit disclosure.

Paper Length and Review Process:

An important directive is regarding the length of the manuscripts. Authors are instructed to confine their primary content to within eight pages, notwithstanding any additional pages required for references. This demarcation aims to standardize the length for ease of review while allowing authors the flexibility to reference comprehensively. Over-length papers, or those manipulating formatting rules, are summarily excluded from review, highlighting the importance of adherence to formatting standards in the peer-review process.

Formatting and Presentation:

The guidelines specify the use of a two-column format for the text, with precise measurements for margins and other elements of the layout. Authors using LaTeX are provided with explicit instructions on maintaining uniformity in sectioning, font styles, and figures. There is specific mention of the use of rulers during the review process, eliminated in the final camera-ready versions, to facilitate referencing specific content locations by reviewers. Clear rules for headings and subheadings contribute to a coherent document structure, enhancing readability.

Anonymization for Blind Review:

The document stresses the proper methods of anonymization, a critical element in maintaining objectivity and fairness in the blind review process. It clarifies common misconceptions, advising authors to avoid self-referential pronouns in citations while still allowing references to personal previous work. The goal is to obscure author identities without rendering the text unreadable or incomplete.

Technical Elements:

Numerous technical elements are addressed, including equation numbering, proper citing of references using inline square brackets, and the conventions for presenting figures and tables. LaTeX-specific instructions are provided to automate aspects of formatting so that authors can focus more on the research content. Moreover, the guidelines advise on the appropriate usage of color in graphics, emphasizing accessibility for readers with color vision deficiencies.

Implications and Future Considerations

The structured guidelines play a crucial role in maintaining the high-quality standards expected of CVPR publications. By standardizing formatting, the guidelines help facilitate the review process, making it more efficient and consistent. This contributes to the reliability and reproducibility of scientific communications in computer vision, allowing researchers to focus more on content than on publication logistics.

Although these guidelines provide significant structure, the dynamic field of publishing might necessitate additional considerations in future iterations. The templates should continue evolving to incorporate more automation tools and address novel forms of content presentation, such as interactive graphics or datasets, that are becoming increasingly important in computer vision research.

Conclusion

In summary, the LaTeX Author Guidelines for CVPR Proceedings constitute an essential framework for researchers intending to submit to this prestigious conference. By meticulously delineating the stylistic and structural norms, the document ensures that submissions are not only technically compliant with IEEE standards but also readily comparable during the peer-review process. The adherence to these precise guidelines reflects a commitment to quality and consistency in academic publishing, supporting the advancement of research within the computer vision community.