Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals (2102.06191v3)

Abstract: Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual domain. In this paper, we make a first attempt to tackle the problem on datasets that have been traditionally utilized for the supervised case. To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings. This marks a large deviation from existing works that relied on proxy tasks or end-to-end clustering. Additionally, we argue about the importance of having a prior that contains information about objects, or their parts, and discuss several possibilities to obtain such a prior in an unsupervised manner. Experimental evaluation shows that our method comes with key advantages over existing works. First, the learned pixel embeddings can be directly clustered in semantic groups using K-Means on PASCAL. Under the fully unsupervised setting, there is no precedent in solving the semantic segmentation task on such a challenging benchmark. Second, our representations can improve over strong baselines when transferred to new datasets, e.g. COCO and DAVIS. The code is available.

• The paper details comprehensive formatting instructions, from abstract placement to two‐column layouts, ensuring adherence to CVPR standards.
• It outlines the submission process including length restrictions, dual submission rules, and mandatory copyright forms to maintain review integrity.
• It emphasizes clear presentation through numbered equations, consistent figure formatting, and tools like a printed ruler to aid reviewer feedback.

Overview of the Paper: \LaTeX\ Author Guidelines for CVPR Proceedings

This paper primarily serves as a comprehensive guideline for authors preparing manuscripts for submission to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). It offers meticulous instructions on the usage of \LaTeX\ in alignment with the formatting and style conventions mandated by the CVPR proceedings.

Key Elements of the Paper

1. Abstract Formatting: The paper emphasizes the placement and formatting of the abstract, underscoring its position at the top of the left-hand column and specifying text alignment, font size, and style.
2. Manuscript Submission Process: Detailed procedures are delineated for submitting papers, including language requirements and stipulations around dual submissions. Crucially, the paper specifies a maximum length of eight pages for the main content, with references not contributing to the page count. The guideline underscores that non-compliant, overlength papers will not be reviewed.
3. Use of Ruler: To facilitate precise communication between reviewers and authors, the style guide prescribes the integration of a printed ruler in submissions. This tool is intended to ease the process of pointing reviewers to particular lines of interest within the document.
4. Mathematics and Equation Numbering: Authors are advised on the importance of numbering equations to allow for subsequent referencing. This practice anticipates the diverse needs of future readers who may require specific referencing absent in the original text.
5. Blind Review Protocols: Advice is given on maintaining anonymity during the blind review process. Clarity is provided on how previous work can be cited without compromising the double-blind nature of the review process.
6. Document Layout and Formatting: The paper sets detailed requirements concerning the two-column format, spacing, margins, and headers to be used, along with type-style and font preferences. Specific attention is paid to the format of headings and textual components to ensure visual cohesion and clarity.
7. Figures, Tables, and Graphics: There are detailed instructions for formatting and integrating visual elements into the manuscript, with emphasis on readability both digitally and in print. Authors are advised on the adjustment of font sizes within figures to maintain consistency with the manuscript's main text.
8. Final Copy and Submission: The paper concludes by discussing requirements for final submissions, particularly the necessity of including a signed IEEE copyright release form.

Implications and Future Considerations

While the paper does not make specific advances in computer science research methodology, it is integral to the dissemination of knowledge within the scientific community by ensuring standardization in manuscript preparation. The guidelines provided enhance the readability, accessibility, and reviewability of submissions to CVPR, thereby supporting the robustness and integrity of the peer review process.

Looking ahead, as typesetting technologies and document preparation systems evolve, there may be implications for how formatting guidelines are developed and enforced. The integration of more automated formatting tools could potentially streamline the submission process and reduce the preparatory burden on authors. Additionally, the emphasis on guidelines like these raises broader considerations about how standardized formats impact accessibility and the broader dissemination of scientific knowledge.

In summary, this paper functions as an essential directive for authors aiming to contribute to CVPR, defining a structured approach to manuscript preparation that underpins consistency and professionalism in conference submissions.