ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network (1811.11431v3)

Abstract: We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (1) object classification, (2) semantic segmentation, (3) object detection, and (4) LLMing. Experiments on these tasks, including image classification on the ImageNet and LLMing on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at https://github.com/sacmehta/ESPNetv2

• The paper outlines essential author guidelines to ensure uniform manuscript preparation for CVPR submissions.
• It specifies strict formatting rules, including page limits, font, margin settings, and reference numbering for clarity.
• The guidelines promote a fair double-blind review process and suggest potential for future automation in compliance checks.

Overview of CVPR \LaTeX\ Author Guidelines

The paper under discussion provides specific author guidelines for manuscript preparation for the CVPR (Conference on Computer Vision and Pattern Recognition) proceedings. These instructions focus on ensuring uniformity and precision in document formatting, catering to both content clarity and review process efficiency, particularly within the context of a double-blind peer review system.

Key Considerations in Manuscript Preparation

The document outlines several crucial aspects authors must adhere to when preparing their submissions:

• Language and Dual Submissions: All manuscripts are to be submitted in English. The guidelines provide a detailed overview of the CVPR policies surrounding dual submissions, advising authors to refer to the official web page for comprehensive understanding.
• Page and Format Specifications: The document emphasizes strict adherence to page limits, with papers restricted to eight pages excluding references. Importantly, any deviation from the prescribed formatting, such as altering margins or fonts, results in disqualification from the review process.
• Blind Review Protocol: The paper clarifies the misunderstanding around anonymizing submissions. Authors must avoid personal pronouns when citing their prior work, maintaining anonymity while ensuring traceability of intellectual contributions.
• Mathematical Expression and Section Numbering: Authors are instructed to number sections and equations for contextual referencing. This is crucial for readers who may need to refer to specific equations, enhancing the clarity and usability of the document.

Practical Implications

The detailed formatting guidelines serve both authors and reviewers by standardizing manuscript submissions, which facilitates a smoother review process. Authors benefit from clear instructions that eliminate ambiguity regarding submission expectations, while reviewers are provided with a consistent document format that simplifies navigation and evaluation.

From a practical standpoint, adherence to these guidelines aids in maintaining the quality and consistency of published works within the CVPR proceedings. It fosters a level playing field for submissions, irrespective of the authors' affiliations or resources.

Speculative Implications for Future Research

The discipline in formatting may encourage future developments in automated tools for checking compliance, thereby reducing the administrative burden on authors. Such advancements could streamline the submission process and improve adherence to guidelines.

Furthermore, as AI progresses, there may be potential for more sophisticated tools to assess not just the format but the content of the manuscripts for originality and contribution to the field, enhancing the overall quality and relevance of conference proceedings.

In conclusion, the CVPR \LaTeX\ Author Guidelines document is an essential resource for authors aiming to contribute to one of the leading conferences in computer vision. By laying out precise formatting and submission rules, it ensures a structured and equitable review process conducive to high-quality scientific discourse.