Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection (2103.17115v1)

Abstract: Conventional deep learning based methods for object detection require a large amount of bounding box annotations for training, which is expensive to obtain such high quality annotated data. Few-shot object detection, which learns to adapt to novel classes with only a few annotated examples, is very challenging since the fine-grained feature of novel object can be easily overlooked with only a few data available. In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem. Built on the meta-learning based framework, Dense Relation Distillation module targets at fully exploiting support features, where support features and query feature are densely matched, covering all spatial locations in a feed-forward fashion. The abundant usage of the guidance information endows model the capability to handle common challenges such as appearance changes and occlusions. Moreover, to better capture scale-aware features, Context-aware Aggregation module adaptively harnesses features from different scales for a more comprehensive feature representation. Extensive experiments illustrate that our proposed approach achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Code will be made available at https://github.com/hzhupku/DCNet.

• The paper introduces a dense relation distillation framework with context-aware aggregation that significantly boosts few-shot object detection performance.
• It details a novel design for integrating rich relational features and multi-scale context to enhance learning with limited data.
• The approach outperforms conventional methods by effectively transferring relational knowledge, paving the way for future innovations in few-shot detection.

A Technical Overview of the \LaTeX\ Guidelines for Author Response in CVPR

The document titled "LaTeX Guidelines for Author Response" serves as a procedural guide for authors preparing a rebuttal for papers submitted to the Conference on Computer Vision and Pattern Recognition (CVPR). This overview dissects the stipulated guidelines and explores the broader implications for the academic community.

Objectives and Scope

The primary objective of the author rebuttal is to address and rectify factual inaccuracies in the reviewers' comments and to provide additional information that may have been requested. Importantly, this rebuttal process is an optional exercise for authors, adhering to similar procedural constraints observed in past CVPR conferences. Rebuttals are explicitly prohibited from introducing new contributions or extensively altering the original content of the paper, maintaining the integrity and scope of initial submissions.

Response Structure

• Response Length: The guidelines dictate that the response must not exceed one page, inclusive of references and figures. Responses surpassing this limit or those that significantly deviate from the prescribed formatting will not be reviewed. This strict restriction underscores the importance of brevity and focus within the rebuttal content.
• Formatting: Comprehensive formatting instructions are provided to ensure uniformity and readability. The response should adopt a two-column format with specific dimensions for text areas, margins, and spacing. Such meticulous attention to detail ensures consistency across submissions, facilitating easier review and assessment.

Formatting Specifics

In line with ensuring consistency, the document prescribes a number of formatting conventions:

• Text and Typography: All text is to be rendered in Times or Times Roman, with main text set in 10-point font size. Heading fonts may vary slightly in size (10 or 12 point) as per standard document hierarchy.
• Graphics and Illustrations: Authors are instructed to use \verb+\includegraphics+ for placing figures in the document. This aligns with common LaTeX best practices and ensures that the graphic dimensions adhere to the column width to maintain visual coherence.

Policy Implications

The clarity with which these guidelines detail the rebuttal formatting requirements reflects an ongoing commitment to maintain a streamlined review process in academic publishing. Such procedural clarity also ensures authors and reviewers commit to a standard of professionalism, indirectly fostering an environment that values precision and clarity over volume.

Future Directions

Although the document itself does not experiment with novel review methodologies, the precise guidelines it sets forth pave the way for further advancements in how academic discourse is conducted. Future developments may explore more dynamic guidelines allowing for greater flexibility in addressing the nuances of increasingly complex research. The current framework promotes a rigorous editorial standard, which is foundational for evaluating potential enhancements in peer review processes.

Conclusion

The "LaTeX Guidelines for Author Response" document is a critical component in the structured framework of CVPR's paper review process. Its detailed provisions ensure that rebuttals are concise, focused, and formatted in a manner that enhances readability and uniformity. As the academic community continues to evolve, documents such as these foster a collaborative yet regulated environment conducive to scholarly discourse. The ongoing adherence to these guidelines represents a commitment to maintaining high academic standards while acknowledging the growing complexity of computer vision research.