Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization (2103.04523v2)

Abstract: Weakly supervised object localization(WSOL) remains an open problem given the deficiency of finding object extent information using a classification network. Although prior works struggled to localize objects through various spatial regularization strategies, we argue that how to extract object structural information from the trained classification network is neglected. In this paper, we propose a two-stage approach, termed structure-preserving activation (SPA), toward fully leveraging the structure information incorporated in convolutional features for WSOL. First, a restricted activation module (RAM) is designed to alleviate the structure-missing issue caused by the classification network on the basis of the observation that the unbounded classification map and global average pooling layer drive the network to focus only on object parts. Second, we designed a post-process approach, termed self-correlation map generating (SCG) module to obtain structure-preserving localization maps on the basis of the activation maps acquired from the first stage. Specifically, we utilize the high-order self-correlation (HSC) to extract the inherent structural information retained in the learned model and then aggregate HSC of multiple points for precise object localization. Extensive experiments on two publicly available benchmarks including CUB-200-2011 and ILSVRC show that the proposed SPA achieves substantial and consistent performance gains compared with baseline approaches.Code and models are available at https://github.com/Panxjia/SPA_CVPR2021

• The paper provides a detailed technical manual for preparing research manuscripts for the CVPR conference proceedings using LaTeX, covering essential formatting, structure, and submission requirements.
• It specifies strict formatting requirements including a two-column layout, precise page dimensions, required font usage (Times/Times Roman), and an eight-page limit excluding references.
• The guidelines emphasize compliance with blind review and dual submission policies, instructing authors to anonymize submissions and include a signed IEEE copyright release form for publication.

Author Guidelines for CVPR Proceedings: A Technical Overview

The paper "LaTeX Author Guidelines for CVPR Proceedings" serves as a comprehensive technical manual for authors preparing manuscripts for the Computer Vision and Pattern Recognition (CVPR) conference. CVPR maintains rigorous standards to ensure consistent and high-quality presentation of research across its proceedings. This document is pivotal for researchers aiming to participate in CVPR and requires adherence to specific formatting, language, and submission guidelines.

Manuscript Preparation

The manuscript should be prepared in LaTeX, following a structured approach delineated in the guidelines. Core aspects include:

• Abstract and Main Text: The abstract should be concise and italicized, providing a clear synopsis of the research. The main text must adhere to a two-column format with a rigid structure for headings and sections to ensure readability and uniformity.
• Page and Font Specifications: The document's allowable width and height are explicitly defined as 6-7/8 inches by 8-7/8 inches, respectively, with detailed margin settings. The font requirements mandate Times or Times Roman across different sections to maintain consistency.

Review and Submission Process

The guidelines emphasize compliance with the dual submission policy and blind review process, ensuring an anonymous review of the manuscripts. Authors are instructed not to disclose their identity in the text while citing previous work. Properly anonymizing submissions is crucial to eliminate identity cues and maintain the integrity of the review process.

• Paper Length: There is a strict eight-page limit on manuscript length excluding references. Overlength papers are non-compliant and excluded from the review, highlighting the importance of precise content distillation and clarity.
• Ruler and Equation Numbering: The use of a printed ruler in the document is advised to facilitate precise reviewer comments. Moreover, consistent numbering of sections and equations ensures ease of reference for future readers, maintaining a seamless and professional document structure.

Structural and Stylistic Elements

The paper provides detailed instructions on styling elements such as footnotes, illustrations, captions, and references:

• Figures and Tables: All graphics must be centered and readable in print, with captions in a smaller Roman type to integrate seamlessly with the text. The appropriate use of LaTeX commands for resizing images is recommended to maintain document aesthetics.
• Color Usage: Authors must ensure compatibility with potential printing limitations, as some readers may resort to hardcopy versions, necessitating clarity without reliance on color.

Final Deliverable and Implications

The guidelines conclude with instructions for submitting the final copy, specifically including the requirement for a signed IEEE copyright release form, an essential compliance step for publication. The formal process outlined by CVPR ensures that all submissions adhere to a stringent standard, thus preserving the conference's reputation as a preeminent venue for disseminating cutting-edge computer vision research.

Overall, adherence to these detailed guidelines facilitates a standardized and scholarly presentation of research, providing CVPR participants with the necessary framework to prepare impactful and compliant manuscripts. Future developments in the field of AI and computer vision will continue to be shared through these meticulously structured and peer-reviewed proceedings.