Domain Adaptation for Structured Output via Discriminative Patch Representations (1901.05427v4)

Abstract: Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks. However, models trained on one data domain may not generalize well to other domains without annotations for model finetuning. To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain. We propose to learn discriminative feature representations of patches in the source domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space. With such representations as guidance, we use an adversarial learning scheme to push the feature representations of target patches in the clustered space closer to the distributions of source patches. In addition, we show that our framework is complementary to existing domain adaptation techniques and achieves consistent improvements on semantic segmentation. Extensive ablations and results are demonstrated on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.

Overview of ICCV Author Guidelines

The presented document outlines the comprehensive guidelines for authors submitting manuscripts to the International Conference on Computer Vision (ICCV). These guidelines are pivotal in ensuring the consistency and standardization of papers published in the proceedings of such a notable conference. The document meticulously details the requirements related to formatting, submission policies, paper length, and the preservation of the blind review process.

Key Components of the Guidelines

Submission Criteria: The document underscores that all submissions must adhere to a maximum of eight pages, excluding references. Importantly, there are no extra page charges, highlighting a straightforward policy that encourages succinctness without a financial penalty.

Format Specifications: Authors are mandated to follow a two-column format for the text, which includes strict instructions on margins, font types, and sizes. This ensures not only a uniform aesthetic but also aids in the clarity and readability of complex technical information.

Blind Review Process: A significant portion of the guidelines is dedicated to maintaining the integrity of the blind review process. It explicitly instructs authors on the correct method of referring to their previous work without compromising anonymity, an aspect critical to objective peer reviews.

Mathematical Consistency: Emphasis is placed on the numbering of sections and equations, which facilitates ease of reference and enhances the academic rigor of the manuscripts.

Use of Color and Graphics: Although not central to textual content, the guidelines carefully consider the effective use of graphical elements and color. This is relevant in the context of both electronic and printed copies, given the diverse modes of accessing academic content.

Practical and Theoretical Implications

Practically, these guidelines aim to standardize the presentation of research, thus easing the review process and subsequent readership. They serve as a foundation to present complex computer vision research in a manner that is not only accessible but also adheres to a high level of academic rigor. Theoretically, following such guidelines ensures that the dissemination of research adheres to established conventions that uphold the integrity and quality of scientific communication.

Speculation on Future Developments in AI

The meticulous structure enforced by these guidelines indirectly influences the presentation of advancements in AI and computer vision. As AI continues to evolve, the expectations for documenting and presenting these findings will likely demand even finer precision and uniformity. There may be future revisions, especially in addressing new types of data visualization techniques and perhaps adaptive formatting for various digital platforms.

In summary, the ICCV author guidelines serve as an integral framework for researchers. They ensure that contributions to the field are not only technically sound but also accessible and standardized for a global audience. Such guidelines continue to play a crucial role in navigating the increasing complexity of AI research dissemination.