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