Adaptive Semantic-Visual Tree for Hierarchical Embeddings (2003.03707v1)

Abstract: Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make predictions less ambiguous. However, previous studies of fine-grained image retrieval primarily focus on semantic similarities or visual similarities. In a real application, merely using visual similarity may not satisfy the need of consumers to search merchandise with real-life images, e.g., given a red coat as a query image, we might get a red suit in recall results only based on visual similarity since they are visually similar. But the users actually want a coat rather than suit even the coat is with different color or texture attributes. We introduce this new problem based on photoshopping in real practice. That's why semantic information are integrated to regularize the margins to make "semantic" prior to "visual". To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously. The semantic information satisfies the demand of consumers for similar merchandise with the query while the visual information optimizes the correlations within the semantic class. At each level, we set different margins based on the semantic hierarchy and incorporate them as prior information to learn a fine-grained feature embedding. To evaluate our framework, we propose a new dataset named JDProduct, with hierarchical labels collected from actual image queries and official merchandise images on an online shopping application. Extensive experimental results on the public CARS196 and CUB-

• The paper presents an in-depth guide on the acmart LaTeX class, outlining diverse template styles and parameters for various ACM publications.
• It details the mandatory use of the Libertine typeface and standardized metadata practices to ensure uniformity and reduce formatting errors.
• The study reinforces structured publishing processes that enhance metadata extraction, consistency, and overall efficiency in scholarly documentation.

Analysis of the ACM LaTeX Document Class Paper

The document under review is an academic paper presented at the 27th ACM International Conference on Multimedia, addressing intricacies of the ``acmart'' LaTeX document class used widely across ACM publications. Authored by a distinguished team of computer science professionals, this paper provides a technical overview of the standardized document preparation system integral to ensuring consistency, accessibility, and metadata extraction in scientific documentation published by ACM.

Core Components and Contributions

The paper meticulously outlines the versatility of the ``acmart'' document class, capable of formatting a range of publication types from full conference proceedings and journals to SIGCHI Extended Abstracts and SIGGRAPH Emerging Technology abstracts. A pragmatic guide, it covers aspects such as:

• Template Styles: Detailing distinct template styles such as acmsmall, acmlarge, and acmtog for journals, versus acmconf, sigchi, and sigplan for conferences. Each style is tailored to the unique formatting requirements of the respective publication type.
• Template Parameters: Offering a toolkit of parameters like anonymous,review for double-blind submissions, enhancing both flexibility and customization according to submission requirements.
• Mandatory Typeface Usage: Emphasizing the necessity to adhere to the Libertine typeface family, ensuring uniformity across publications and restricting unsanctioned modifications to maintain standardization.

The paper advocates a structured approach to author metadata, rights management, and sectioning commands, all aimed at enhancing the user experience for researchers involved in the manuscript preparation process.

Critical Evaluation and Insights

In essence, this paper serves as an operational manual rather than a conventional research article. It contributes significantly by educating authors, both novices and publishing veterans, on the efficient use of LaTeX for preparing ACM-ready manuscripts. The strict adherence to template instructions improves the publication pipeline process by reducing formatting errors and easing the transition from draft to publication-ready documents.

The inclusion of technical guidance on structuring tables, figures, and mathematical equations is an indispensable resource for authors seeking to comply with ACM standards. It also highlights the importance of maintaining data integrity and reproducibility of results through standardized documentation practices.

Practical and Theoretical Implications

Practically, adoption of the ``acmart'' document class streamlines publication workflows, promoting enhanced researcher productivity by minimizing formatting-related obstacles. This framework ensures document uniformity, a critical factor when dealing with large volumes of academic submissions. The enforcement of strict submission guidelines ensures efficiency and quality control across ACM outputs.

Theoretically, the systematic approach espoused reflects ACM's commitment to metadata-enabled digital libraries and future-proofing scholarly communications. This methodological transparency facilitates subsequent metadata extraction and indexing in digital repositories, potentially easing access and discoverability of research outputs.

Future Developments

The evolution of such document templates could focus on incorporating advanced features aligned with emerging digital technologies. Integration with collaborative platforms could promote real-time co-authorship and peer review processes, further streamlining the research publication lifecycle. As artificial intelligence continues to develop, future iterations of the ``acmart'' class might leverage machine learning algorithms to provide automated formatting feedback, enhancing document consistency even further.

In conclusion, the paper encapsulates an essential technical discourse for the computer science research community regarding efficient publication practices. It ensures that authors are equipped with the required tools to adhere to ACM's high standards of documentation, thus contributing to the broader dissemination and impact of scientific research.