To bridge this crucial gap, first, this paper presents a comprehensive benchmark suite to evaluate the out-of-distribution (OOD) performance of image compression methods.

Figure 1: R-Blur adds Gaussian noise to image (a) with the fixation point (red dot) to obtain (b).

Due to error propagation among neighboring frames, more amount of corruption can be observed in the decoded frame sequences of each Figure.

Equally contribute to this research.

BART had higher robustness against text corruptions on the GQA dataset. This may be due to the different language encoders used in BART and T5.Among all adaptation The top row shows the robustness against image corruptions and the bottom row is results against text corruptions. We also choose different embedding dimensions for adapter-based methods. The relative robustness of adaptation methods based on CLIP-T5 is presented in Table 1. Figure 2

The real-time estimation of time-varying parameters from high-dimensional, heavy-tailed and corrupted data-streams is a common sub-routine in systems ranging from those for network monitoring and anomaly detection to those for traffic scheduling in data-centers.