Benchmarking the Robustness of Referring Perception Models under Perturbations

Core Concepts

The author emphasizes the importance of assessing the robustness of referring perception models against various perturbations to ensure reliable real-world applications.

Abstract

The content discusses R2-Bench, a benchmark for evaluating the robustness of referring perception models. It introduces a taxonomy of perturbations, a perturbation synthesis toolbox, and R2-Agent for automated evaluation. The experiments analyze the impact of perturbations on different tasks and provide insights into model vulnerabilities. Key points: Referring perception models empower intelligent systems with object grounding based on guidance. Real-world disturbances like noise, errors, and limitations affect model performance. R2-Bench assesses model robustness across five key tasks using diverse perturbations. The R2-Agent automates model evaluation based on human instructions. Perturbation analysis reveals varying impacts on model performance across different types. Correlation matrices show unique effects of perturbations on model degradation. Dynamic perturbations in videos lead to more significant performance drops than static ones.

Stats

Conducting a rigorous analysis of RPMs’ robustness to a wide array of perturbations is necessary for building reliable real-world applications. A total of 32 types of noises are considered in this paper.

Quotes

Key Insights Distilled From

$\text{R}^2$-Bench

by Xiang Li,Kai... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.04924.pdf

Deeper Inquiries

Which noises have small impact

In the context of perturbations in referring perception models, some noises have a small impact on model performance. Specifically, visual perturbations like brightness (BR) and saturate (SA), as well as acoustic perturbations such as gaussian noise (GN) and impulse noise (IN), are examples of noises that exhibit minimal impact on the overall performance of the models. These noises may not significantly disrupt the model's ability to accurately ground objects based on referring guidance.

Why is the noisy figure incorrect

The noisy figure is incorrect due to the presence of fog effect in an indoor virtual meeting scenario. The fog effect is incongruent with an indoor setting and can lead to misinterpretation or misclassification by the model. In this specific case, having a fog effect when referencing an indoor environment introduces confusion and hinders accurate object grounding based on textual descriptions or other forms of guidance.

How can we resolve it

To resolve the issue with the noisy figure being incorrect in an indoor virtual meeting scenario, we should remove or adjust the inappropriate perturbation effects that do not align with the given instruction. In this case, removing the fog effect from visual input data samples associated with an indoor setting would be necessary for ensuring accurate object segmentation and grounding within a virtual meeting environment. By eliminating irrelevant or conflicting perturbations like fog in this context, we can improve model performance and enhance its ability to interpret instructions correctly for real-world applications.