- This paper investigates the issue of robustness in video action recognition, but it lacks comparison with test-time adaptation (TTA) methods, such as [A-B]. These TTA methods also aim to adapt to out-of-distribution data when the input data is disturbed by noise. Although these TTA methods mainly focus on updating model parameters, and this paper primarily focuses on adjusting the input data, how to prove that data processing is superior to model parameter adjustment? I believe a comparison should be made based on experimental results.
- Under noisy conditions, many TTA methods can achieve desirable results, while the improvement brought by this paper's method is relatively low.
- In appendix A.2.1, under noisy conditions, the average performance improvement brought by this paper's method is very low and can even be counterproductive under certain noise conditions. Does this indicate an issue with the approach of changing input data?
- How to verify the reliability of the long-range photometric consistency in section 3.3? Are there any ablation study results reflecting the performance gain brought by each part?
- The explanation of the formula content in Algorithm 1 in the main body is not clear enough.
[A] Temporal Coherent Test-Time Optimization for Robust Video Classification. ICLR23
[B] Video Test-Time Adaptation for Action Recognition. CVPR23
- This paper investigates the issue of robustness in video action recognition, but it lacks comparison with test-time adaptation (TTA) methods, such as [A-B]. These TTA methods also aim to adapt to out-of-distribution data when the input data is disturbed by noise. Although these TTA methods mainly focus on updating model parameters, and this paper primarily focuses on adjusting the input data, how to prove that data processing is superior to model parameter adjustment? I believe a comparison should be made based on experimental results.