Paper: B$^{3}$CT: Three-branch Coordinated Training for Domain Adaptive Semantic Segmentation (ICLR-2024)

Reviewer comment (Experiments):
Although the authors state in the main text, "the third branch of which facilitates learning domain-invariant features for alignment", they provide no experimental results.

Author response:
**Q1: Experimental results of the third branch.**

**A1:** As you mentioned, ablation experiments on the third branch are very necessary. Our method is a three-branch collaborative training method. If only the third branch that uses hybrid attention for calculation is added, the performance will also be improved. We have included this experiment in Tab. 1 of the paper. The table is also demonstrated below. Adding only the third branch can bring an improvement of 0.4 mIoU. The improvement effect is not significant because this branch needs to collaborate with other modules. Only by adding all three components can the best effect be achieved.

#### Component Ablation for B3CT

| hybrid branch | coor.weight | AAC |   mIoU   |
|-|-|-|-|
| - | - |  -  |   73.8   |
| ✓ | - |  -  |   74.2   |
| ✓ | ✓ |  -  |   74.3   |
| ✓ | ✓ |  ✓  | **74.8** |

Reviewer comment (Experiments):
In Tab. 1, the result of only ablating "coor. weight" also should be reported.

Author response:
**Q2: Results of only ablating coordinate weight.**

**A2:** We apologize for any lack of clarity in our initial description. In our B3CT framework, coordinate weight is employed to dynamically adjust the contribution of the third branch during model training. Therefore, applying coordinate weight necessitates the inclusion of the third branch. Additionally, we conducted ablation experiments with different hyperparameters for the coordinate weight $\alpha$, and the specific results are shown in Figure 4.

Reviewer comment (Evaluation):
In Sec. 4.4, the authors should differentiate the comparison results according to different network architectures, such as deeplab and segformer.

Author response:
**Q3: The comparison results of different network architectures.**

**A3:** Investigating the generalization ability of a model is crucial. We applied the B3CT model using DAFormer as the backbone on two benchmarks, GTA5 $\rightarrow$ Cityscapes and Synthia $\rightarrow$ Cityscapes respectively. The experimental results are shown in the table below and updated in Tab. 5. B3CT achieves a 1.5 and 1.7 percent point improvement over the respective two benchmarks. This implies that B3CT has the flexibility to integrate with various Transformer-based models seamlessly. 

#### Further baseline comparison on GTA5 $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| DAFormer    | 95.7 | 70.2 | 89.4 | 53.5 | 48.1 | 49.6 | 55.8 | 59.4 | 89.9 | 47.9 | 92.5 | 72.2 | 44.7 | 92.3 | 74.5 | 78.2 | 65.1 | 55.9 | 61.8 | 68.3 |
| HRDA | 96.4 | 74.4 | 91.0 | **61.6**    | 51.5 | 57.1 | 63.9 | **69.3**    | 91.3 | 48.4 | 94.2 | 79.0 | 52.9 | 93.9 | **84.1**    | 85.7 | 75.9 | **63.9**    | 67.5 | 73.8 |
| DAFormer + B3CT | **96.5**    | **75.2**    | 90.7 | 60.6 | 45.5 | 57.5 | 63.7 | 68.6 | 90.9 | 48.4 | 91.9 | 76.7 | 49.2 | 92.1 | 59.5 | 68.3 | 64.6 | 58.7 | 67.9 | 69.8 |
| HRDA + B3CT | **96.5**    | 75.0 | **91.2**    | 59.6 | **55.4**    | **58.5**    | **66.3**    | 69.1 | **91.9**    | **49.3**    | **94.4**    | **79.2**    | **55.3**    | **94.2**    | 83.2 | **88.7**    | **80.3**    | 63.7 | **68.4**    | **74.8**   |

#### Further baseline comparison on Synthia $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| DAFormer    | 84.5 | 40.7 | 88.4 | 41.5 | 6.5  | 50.0 | 55.0 | 54.6 | 86.0 | -- | 89.8 | 73.2 | 48.2 | 87.2 | -- | 53.2 | -- | 53.9 | 61.7 | 60.9 |
| HRDA | 85.2 | 47.7 | **88.8**    | **49.5**    | 4.8 | 57.2 | 65.7 | 60.9 | 85.3 | -- | 92.9 | 79.4 | 52.8 | **89.0**    | -- | **64.7**    | -- | 63.9 | 64.9 | 65.8 |
| DAFormer + B3CT | 82.8 | 44.2 | 86.7 | 38.9 | 5.0 | 55.0 | 63.0 | 61.2 | 83.6 | -- | 82.9 | 76.3 | 47.9 | 88.0 | -- | 58.6 | -- | 61.8 | 65.3 | 62.6 |
| HRDA + B3CT | 90.5 | 57.5 | 88.7 | 46.3 | **7.5**     | **57.7**    | **66.6**    | **63.3**    | **89.1**    | -- | **94.2**    | **80.5**    | **55.8**    | **89.0**    | -- | 54.4 | -- | **64.6**    | **65.7**    | **67.0**  |

Reviewer comment (Experiments):
Recent works not only conduct experiments on two standard simulation-to-real benchmarks, i.e., GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes but also extend to adverse conditions. To name a few, SePiCo [a] and MIC [b] have extended to more challenging daytime-to-nighttime semantic segmentation task and CoTTA [c] has also investigated clear-to-adverse conditions using online adaptation. I would like to see the potential of B$^3$CT on more challenging scenarios.

Author response:
**Q4: Experiments on more challenging scenarios.**

**A4:** To assess the generalization capabilities of our proposed method across diverse scenarios, we conducted additional validation on the clear-to-adverse-weather Cityscapes $\to$ ACDC benchmark, which involves domain adaptation from clear weather conditions in Cityscapes to adverse weather conditions. Our re-implementation of HRDA achieves 64.3 mIoU on the test set, which is basically consistent with third-party re-implementation results. In comparison to this, B3CT further enhances performance to 66.9 mIoU. These experimental results prove the robustness of our B3CT. More detailed experimental results can be found in the table below and App. A.1.

#### Comparison with existing methods on Cityscapes $\rightarrow$ ACDC

|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|----|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| ADVENT | 72.9 | 14.3 | 40.5 | 16.6 | 21.2 | 9.3 | 17.4 | 21.2 | 63.8 | 23.8 | 18.3 | 32.6 | 19.5 | 69.5 | 36.2 | 34.5 | 46.2 | 26.9 | 36.1 | 32.7 |
| MGCDA | 73.4 | 28.7 | 69.9 | 19.3 | 26.3 | 36.8 | 53.0 | 53.3 | **75.4**    | 32.0 | 84.6 | 51.0 | 26.1 | 77.6 | 43.2 | 45.9 | 53.9 | 32.7 | 41.5 | 48.7 |
| DANNet    | 84.3 | 54.2 | 77.6 | 38.0 | 30.0 | 18.9 | 41.6 | 35.2 | 71.3 | 39.4 | **86.6**    | 48.7 | 29.2 | 76.2 | 41.6 | 43.0 | 58.6 | 32.6 | 43.9 | 50.0 |
| DAFormer | 58.4 | 51.3 | 84.0 | 42.7 | 35.1 | 50.7 | 30.0 | 57.0 | 74.8 | 52.8 | 51.3 | 58.3 | 32.6 | 82.7 | 58.3 | 54.9 | 82.4 | 44.1 | 50.7 | 55.4 |
| HRDA | 69.1 | **64.8** | 86.4 | **53.9**    | 40.3 | **56.5**    | 41.1 | **63.9**    | 72.8 | **58.8**    | 62.0 | **69.9**    | 44.2 | 87.0 | **73.5**    | 82.0 | 87.8 | 45.4 | **63.0**    | 64.3 |
| HRDA + B3CT | **89.8**    | 62.0 | **86.6**    | 53.5 | **40.7**    | 48.9 | **58.8**    | 60.1 | 73.2 | 57.1 | 85.7 | 67.9 | **44.6**    | **87.9**    | 72.5 | **84.3**    | **88.4**    | **51.6**    | 56.8 | **66.9**    |

Reviewer comment (Evaluation):
Insufficient justifications: For example, about training accuracy or pseudo-accuracy, some justifications are missing in this paper. Any advantages and limitations of pseudo-accuracy?

Author response:
**Q5: Justifications about pseudo-accuracy.**

**A5:** Our model employs a self-training strategy, where the teacher model generates pseudo-labels to supervise the student model during training. Due to the absence of ground truth in the target domain, assessing the accuracy of pseudo-labels becomes challenging. If the accuracy of pseudo-labels is compromised, enforcing domain alignment on the third branch may lead to network oscillation and convergence issues. We extensively discuss this matter in Sec. 3.3, with the original text citation:

    Since no reliable pseudo-labels of the target domain are available at the beginning of the training, prematurely introducing hybrid attention brings label noise and prevents the model from learning discriminative semantic features. Conversely, introducing hybrid computation late can bias the model toward the source distribution and trap it in a local optimum. 

In summary, using pseudo-labels as a factor for coordinate weight weights is one of its advantages. The possible limitation is that there may be errors due to distribution bias in pseudo-labels, but the outputs of the student model and the teacher model are almost identical. This is an unavoidable issue for UDA tasks because the target domain does not have any ground truth. We will continue to explore this direction in the future to see if there is a more suitable coordinate weight design.

Reviewer comment (Writing):
In Eq. 2 and Eq. 5, $p_t^{i,j,c}$ (target predictions from student model) should be $\hat{y}_t^{i,j}$ (target pseudo-label from teacher model)? Also, in Eq. 7 and Eq. 8 $p_t^i, \hat{y}_t^i$ should be $p_t^{i,j}, \hat{y}_t^{i,j}$, respectively?

Author response:
**Q7: Other minor issues.**

**A7:** Thank you for your meticulous review. We have addressed the mentioned issues accordingly:
- Corrections have been made to the symbols in the equations as identified.

- To facilitate better comparison with previous work, all experimental data have been adjusted to retain one decimal point, ensuring consistency in result formatting.

- Capitalization errors in Section 4.1 have been rectified.

Thank you for your thoughtful comments and suggestions again.

Reviewer comment (Presentation):
The results of the experiments throughout the text are in two retained decimals, except for Table 5, where the authors should be consistent.

Author response:
**Q7: Other minor issues.**

**A7:** Thank you for your meticulous review. We have addressed the mentioned issues accordingly:
- Corrections have been made to the symbols in the equations as identified.

- To facilitate better comparison with previous work, all experimental data have been adjusted to retain one decimal point, ensuring consistency in result formatting.

- Capitalization errors in Section 4.1 have been rectified.

Thank you for your thoughtful comments and suggestions again.

Reviewer comment (Evaluation):
While the authors' motivation offers some insight, the experimental results presented are not entirely convincing. Despite the inclusion of three carefully designed components, the model's performance only improves by 1% mIoU on the GTA to Cityscapes transfer. Moreover, the performance on the Synthia to Cityscapes transfer is even lower than the baseline (HRDA).

Author response:
**A1:** We acknowledge the observed limitations in the performance gain of our method on the GTA5/Synthia $\to$ Cityscapes benchmarks. We believe two factors contribute to this result. Firstly, we utilized the same parameter settings as HRDA without fine-tuning for our method. Additionally, we employed mixed-precision during training to conserve graphics memory, which might have affected performance. We anticipate that further adjustments and application to GPU machines with larger memory capacities could lead to improved performance. Notably, after fine-tuning B3CT parameters, our results on the Synthia $\rightarrow$ Cityscapes benchmark have been updated from 65.8 mIoU to 67.0, as reflected in the revised Tab. 5.

#### Comparison with existing methods on Synthia $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| CBST | 68.0 | 29.9 | 76.3 | 10.8 | 1.4 | 33.9 | 22.8 | 29.5 | 77.6 | -- | 78.3 | 60.6 | 28.3 | 81.6 | -- | 23.5 | -- | 18.8 | 39.8 | 42.6 |
| DACS | 80.6 | 25.1 | 81.9 | 21.5 | 2.9 | 37.2 | 22.7 | 24.0 | 83.7 | -- | 90.8 | 67.6 | 38.3 | 82.9 | -- | 38.9 | -- | 28.5 | 47.6 | 48.3 |
| DPL-Dual | 83.5 | 38.2 | 80.4 | 1.3 | 1.1 | 29.1 | 20.2 | 32.7 | 81.8 | -- | 83.6 | 55.9 | 20.3 | 79.4 | -- | 26.6 | -- | 7.4 | 46.2 | 43.0 |
| SAC | 89.3 | 47.2 | 85.5 | 26.5 | 1.3 | 43.0 | 45.5 | 32.0 | 87.1 | -- | 89.3 | 63.6 | 25.4 | 86.9 | -- | 35.6 | -- | 30.4 | 53.0 | 52.6 |
| CorDA | **93.3**    | **61.6**    | 85.3 | 19.6 | 5.1 | 37.8 | 36.6 | 42.8 | 84.9 | -- | 90.4 | 69.7 | 41.8 | 85.6 | -- | 38.4 | -- | 32.6 | 53.9 | 55.0 |
| ProDA    | 87.8 | 45.7 | 84.6 | 37.1 | 0.6 | 44.0 | 54.6 | 37.0 | 88.1 | -- | 84.4 | 74.2 | 24.3 | 88.2 | -- | 51.1 | -- | 40.5 | 45.6 | 55.5 |
| DAFormer    | 84.5 | 40.7 | 88.4 | 41.5 | 6.5  | 50.0 | 55.0 | 54.6 | 86.0 | -- | 89.8 | 73.2 | 48.2 | 87.2 | -- | 53.2 | -- | 53.9 | 61.7 | 60.9 |
| HRDA | 85.2 | 47.7 | **88.8**    | **49.5**    | 4.8 | 57.2 | 65.7 | 60.9 | 85.3 | -- | 92.9 | 79.4 | 52.8 | **89.0**    | -- | **64.7**    | -- | 63.9 | 64.9 | 65.8 |
| DAFormer + B3CT | 82.8 | 44.2 | 86.7 | 38.9 | 5.0 | 55.0 | 63.0 | 61.2 | 83.6 | -- | 82.9 | 76.3 | 47.9 | 88.0 | -- | 58.6 | -- | 61.8 | 65.3 | 62.6 |
| HRDA + B3CT | 90.5 | 57.5 | 88.7 | 46.3 | **7.5**     | **57.7**    | **66.6**    | **63.3**    | **89.1**    | -- | **94.2**    | **80.5**    | **55.8**    | **89.0**    | -- | 54.4 | -- | **64.6**    | **65.7**    | **67.0**  |

Reviewer comment (Experiments):
The authors only conducted experiments in the relatively simple and ideal scenario of transferring from synthetic datasets to real datasets, neglecting the more convincing Cityscapes to ACDC experiments. I believe that adding this experiment would make the authors' work more robust.

Author response:
**A2:** To assess the generalization capabilities of our proposed method across diverse scenarios, we conducted additional validation on the clear-to-adverse-weather Cityscapes $\to$ ACDC benchmark, which involves domain adaptation from clear weather conditions in Cityscapes to adverse weather conditions. Our re-implementation of HRDA achieves 64.3 mIoU on the test set, which is basically consistent with third-party re-implementation results[1]. In comparison to this, B3CT further enhances performance to 66.9 mIoU. These experimental results prove the robustness of our B3CT. More detailed experimental results can be found in the table below and App. A.1.

[1] CDAC: Cross-domain Attention Consistency in Transformer for Domain Adaptive Semantic Segmentation, ICCV 2023.

#### Comparison with existing methods on Cityscapes $\rightarrow$ ACDC

|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|----|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| ADVENT | 72.9 | 14.3 | 40.5 | 16.6 | 21.2 | 9.3 | 17.4 | 21.2 | 63.8 | 23.8 | 18.3 | 32.6 | 19.5 | 69.5 | 36.2 | 34.5 | 46.2 | 26.9 | 36.1 | 32.7 |
| MGCDA | 73.4 | 28.7 | 69.9 | 19.3 | 26.3 | 36.8 | 53.0 | 53.3 | **75.4**    | 32.0 | 84.6 | 51.0 | 26.1 | 77.6 | 43.2 | 45.9 | 53.9 | 32.7 | 41.5 | 48.7 |
| DANNet    | 84.3 | 54.2 | 77.6 | 38.0 | 30.0 | 18.9 | 41.6 | 35.2 | 71.3 | 39.4 | **86.6**    | 48.7 | 29.2 | 76.2 | 41.6 | 43.0 | 58.6 | 32.6 | 43.9 | 50.0 |
| DAFormer | 58.4 | 51.3 | 84.0 | 42.7 | 35.1 | 50.7 | 30.0 | 57.0 | 74.8 | 52.8 | 51.3 | 58.3 | 32.6 | 82.7 | 58.3 | 54.9 | 82.4 | 44.1 | 50.7 | 55.4 |
| HRDA | 69.1 | **64.8** | 86.4 | **53.9**    | 40.3 | **56.5**    | 41.1 | **63.9**    | 72.8 | **58.8**    | 62.0 | **69.9**    | 44.2 | 87.0 | **73.5**    | 82.0 | 87.8 | 45.4 | **63.0**    | 64.3 |
| HRDA + B3CT | **89.8**    | 62.0 | **86.6**    | 53.5 | **40.7**    | 48.9 | **58.8**    | 60.1 | 73.2 | 57.1 | 85.7 | 67.9 | **44.6**    | **87.9**    | 72.5 | **84.3**    | **88.4**    | **51.6**    | 56.8 | **66.9**    |

Reviewer comment (Experiments):
I have some reservations to the extent that the introduced three-branch and AAC modules may add additional parameters and computational load, potentially boosting the model's Oracle performance. The performance gains claimed by the authors could very well stem from this.

Author response:
**A3:** We attribute the improvement in model performance to the training strategy that selectively aligns features between different domains. **Firstly**, the hybrid branch is only added during the training phase. B3CT and HRDA have identical testing processes where no additional parameters are added.
**Secondly**, our model only brought an increase of 0.4M parameters during the training phase, which can be ignored compared to the overall parameters (86.1M). We have included more detailed supplements in the table below and appendix.

#### Comparison of computational efficiency

| UDA method  | Throughput(img/s) | TFLOPS | Training Params. |
|-|-|-|-|
| DAFormer    | 0.7 | 1.6    | 85.2M   |
| HRDA | 0.8 | 3.3    | 85.7M   |
| HRDA + B3CT | 0.8 | 3.3    | 86.1M   |

Reviewer comment (Novelty):
The paper claims to achieve state-of-the-art performance on GTAV→Cityscapes (74.8), which is not entirely accurate. A previously published paper, MIC (CVPR2023) [1], has achieved a higher performance of 75.9. I noticed the authors did not cite and compare it. Therefore, the claimed contribution should be reconsidered.

Author response:
**A1:** We apologize for the inaccurate description of the state-of-the-art information. We have modified the statement regarding performance. At present, experiments based on HRDA and DAFormer baseline have verified the effectiveness of B3CT. The results are shown as follows. In the future, we will apply this method to more baselines including MIC.

#### Comparison with existing methods on GTA5 $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| DAFormer    | 95.7 | 70.2 | 89.4 | 53.5 | 48.1 | 49.6 | 55.8 | 59.4 | 89.9 | 47.9 | 92.5 | 72.2 | 44.7 | 92.3 | 74.5 | 78.2 | 65.1 | 55.9 | 61.8 | 68.3 |
| HRDA | 96.4 | 74.4 | 91.0 | **61.6**    | 51.5 | 57.1 | 63.9 | **69.3**    | 91.3 | 48.4 | 94.2 | 79.0 | 52.9 | 93.9 | **84.1**    | 85.7 | 75.9 | **63.9**    | 67.5 | 73.8 |
| DAFormer + B3CT | **96.5**    | **75.2**    | 90.7 | 60.6 | 45.5 | 57.5 | 63.7 | 68.6 | 90.9 | 48.4 | 91.9 | 76.7 | 49.2 | 92.1 | 59.5 | 68.3 | 64.6 | 58.7 | 67.9 | 69.8 |
| HRDA + B3CT | **96.5**    | 75.0 | **91.2**    | 59.6 | **55.4**    | **58.5**    | **66.3**    | 69.1 | **91.9**    | **49.3**    | **94.4**    | **79.2**    | **55.3**    | **94.2**    | 83.2 | **88.7**    | **80.3**    | 63.7 | **68.4**    | **74.8**   |

#### Comparison with existing methods on Synthia $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| DAFormer    | 84.5 | 40.7 | 88.4 | 41.5 | 6.5  | 50.0 | 55.0 | 54.6 | 86.0 | -- | 89.8 | 73.2 | 48.2 | 87.2 | -- | 53.2 | -- | 53.9 | 61.7 | 60.9 |
| HRDA | 85.2 | 47.7 | **88.8**    | **49.5**    | 4.8 | 57.2 | 65.7 | 60.9 | 85.3 | -- | 92.9 | 79.4 | 52.8 | **89.0**    | -- | **64.7**    | -- | 63.9 | 64.9 | 65.8 |
| DAFormer + B3CT | 82.8 | 44.2 | 86.7 | 38.9 | 5.0 | 55.0 | 63.0 | 61.2 | 83.6 | -- | 82.9 | 76.3 | 47.9 | 88.0 | -- | 58.6 | -- | 61.8 | 65.3 | 62.6 |
| HRDA + B3CT | 90.5 | 57.5 | 88.7 | 46.3 | **7.5**     | **57.7**    | **66.6**    | **63.3**    | **89.1**    | -- | **94.2**    | **80.5**    | **55.8**    | **89.0**    | -- | 54.4 | -- | **64.6**    | **65.7**    | **67.0**  |

Reviewer comment (Experiments):
The proposed B$^3$CT is only applicable to transformer-based architectures and cannot be employed with CNN-based models.

Author response:
**A2:** Currently, the best methods for UDA segmentation are based on transformers. Therefore, our starting point is to optimize the attention mechanism in transformers. To be more specific, we introduce cross-attention based on the transformer's self-attention to achieve domain alignment. Except for the AAC module, which introduces very few parameters, different attention calculations use exactly the same network weights. Therefore, it can be easily applied to various transformer backbones, such as HRDA and DAFormer. However, because CNN's backbone does not contain the attention mechanism, it is necessary to design other modules and introduce additional adaptations to apply our idea. Thank you for your insightful input, and we will be committed to improving the adaptability of our approach to a wider range of backbone architectures.

Reviewer comment (Evaluation):
B$^3$CT introduces additional computation during the inference stage, which raises concerns about computational efficiency for both training and testing. It would be valuable to include a comparison of computational efficiency in the paper's evaluation and report the results accordingly.

Author response:
**A3:** In response to the inquiry about computational efficiency, we have provided additional information in the appendix regarding the runtime and parameters of HRDA during inference and demonstrate it below. **Firstly**, the hybrid branch is only added during the training phase. It has no adverse impact on inference speed during testing. Therefore, our model has the same throughput (0.8) and TFLOPs (3.3) as HRDA. 
**Secondly**, our model only brought an increase of 0.4M parameter quantity during the training phase, which can be ignored compared to the overall parameter quantity (86.1M).  In summary, our method achieves competitive performance while upholding computational efficiency and ensuring swift inference speed.

#### Comparison of computational efficiency

| UDA method  | Throughput(img/s) | TFLOPS | Training Params. |
|-|-|-|-|
| DAFormer    | 0.7 | 1.6    | 85.2M   |
| HRDA | 0.8 | 3.3    | 85.7M   |
| HRDA + B3CT | 0.8 | 3.3    | 86.1M   |

Reviewer comment (Reproducibility):
Will authors release code?

Author response:
**A4:** The code will be released promptly after acceptance.

Reviewer comment (Evaluation):
In table 5, it seems that the proposed method performs less effectively on Synthia to Cityscapes benchmark. It would be better to provide some insights and analysis to illustrate these results.

Author response:
**Q1: Experiments on Synthia $\rightarrow$ Cityscapes benchmark.**

**A1:** On the two benchmarks, the previous training of B3CT used the same experimental settings as HRDA without making any changes. We have made appropriate adjustments to the parameters for the design of B3CT and increased the original 65.8 mIoU to 67.0 on the Synthia $\rightarrow$ Cityscapes benchmark. Tab. 5 has been updated to the latest performance.

Reviewer comment (Evaluation):
As shown in the ablation studies in Tables 1-3, the performance gains seem not significant. Did the author conduct experiment with multiple random runs or random seeds? It is not clear how much the training randomness affects the performance when the gains are not significant.

Author response:
**Q2: The influence of randomness in experiments.**

**A2:** Thank you for raising this valuable question. Randomness does indeed have a certain impact on the experimental results. We did not include the impact of randomness in the table to better compare it with previous work. Actually, all of the results are averaged over 3 random seeds. We have added this experimental detail in Sec.4. To demonstrate the impact of randomness, we present the variance of the experimental data obtained in Tab. 5 below. 

#### Comparison with existing methods on GTA5 $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| CBST | 91.8 | 53.5 | 80.5 | 32.7 | 21.0 | 34.0 | 28.9 | 20.4 | 83.9 | 34.2 | 80.9 | 53.1 | 24.0 | 82.7 | 30.3 | 35.9 | 16.0 | 25.9 | 42.8 | 45.9 |
| DACS | 89.9 | 39.7 | 87.9 | 30.7 | 39.5 | 38.5 | 46.4 | 52.8 | 88.0 | 44.0 | 88.8 | 67.2 | 35.8 | 84.5 | 45.7 | 50.2 | 0.0 | 27.3 | 34.0 | 52.1 |
| DPL-Dual    | 92.8 | 54.4 | 86.2 | 41.6 | 32.7 | 36.4 | 49.0 | 34.0 | 85.8 | 41.3 | 86.0 | 63.2 | 34.2 | 87.2 | 39.3 | 44.5 | 18.7 | 42.6 | 43.1 | 53.3 |
| SAC | 90.4 | 53.9 | 86.6 | 42.4 | 27.3 | 45.1 | 48.5 | 42.7 | 87.4 | 40.1 | 86.1 | 67.5 | 29.7 | 88.5 | 49.1 | 54.6 | 9.8 | 26.6 | 45.3 | 53.8 |
| CorDA | 94.7 | 63.1 | 87.6 | 30.7 | 40.6 | 40.2 | 47.8 | 51.6 | 87.6 | 47.0 | 89.7 | 66.7 | 35.9 | 90.2 | 48.9 | 57.5 | 0.0 | 39.8 | 56.0 | 56.6 |
| ProDA | 87.8 | 56.0 | 79.7 | 46.3 | 44.8 | 45.6 | 53.5 | 53.5 | 88.6 | 45.2 | 82.1 | 70.7 | 39.2 | 88.8 | 45.5 | 59.4 | 1.0 | 48.9 | 56.4 | 57.5 |
| DAFormer    | 95.7 | 70.2 | 89.4 | 53.5 | 48.1 | 49.6 | 55.8 | 59.4 | 89.9 | 47.9 | 92.5 | 72.2 | 44.7 | 92.3 | 74.5 | 78.2 | 65.1 | 55.9 | 61.8 | 68.3 |
| HRDA | 96.4 | 74.4 | 91.0 | **61.6**    | 51.5 | 57.1 | 63.9 | **69.3**    | 91.3 | 48.4 | 94.2 | 79.0 | 52.9 | 93.9 | **84.1**    | 85.7 | 75.9 | **63.9**    | 67.5 | 73.8 |
| DAFormer + B3CT | **96.5**    | **75.2**    | 90.7 | 60.6 | 45.5 | 57.5 | 63.7 | 68.6 | 90.9 | 48.4 | 91.9 | 76.7 | 49.2 | 92.1 | 59.5 | 68.3 | 64.6 | 58.7 | 67.9 | 69.8 ± 0.4 |
| HRDA + B3CT | **96.5**    | 75.0 | **91.2**    | 59.6 | **55.4**    | **58.5**    | **66.3**    | 69.1 | **91.9**    | **49.3**    | **94.4**    | **79.2**    | **55.3**    | **94.2**    | 83.2 | **88.7**    | **80.3**    | 63.7 | **68.4**    | **74.8** ± 0.3   |

#### Comparison with existing methods on Synthia $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| CBST | 68.0 | 29.9 | 76.3 | 10.8 | 1.4 | 33.9 | 22.8 | 29.5 | 77.6 | -- | 78.3 | 60.6 | 28.3 | 81.6 | -- | 23.5 | -- | 18.8 | 39.8 | 42.6 |
| DACS | 80.6 | 25.1 | 81.9 | 21.5 | 2.9 | 37.2 | 22.7 | 24.0 | 83.7 | -- | 90.8 | 67.6 | 38.3 | 82.9 | -- | 38.9 | -- | 28.5 | 47.6 | 48.3 |
| DPL-Dual | 83.5 | 38.2 | 80.4 | 1.3 | 1.1 | 29.1 | 20.2 | 32.7 | 81.8 | -- | 83.6 | 55.9 | 20.3 | 79.4 | -- | 26.6 | -- | 7.4 | 46.2 | 43.0 |
| SAC | 89.3 | 47.2 | 85.5 | 26.5 | 1.3 | 43.0 | 45.5 | 32.0 | 87.1 | -- | 89.3 | 63.6 | 25.4 | 86.9 | -- | 35.6 | -- | 30.4 | 53.0 | 52.6 |
| CorDA | **93.3**    | **61.6**    | 85.3 | 19.6 | 5.1 | 37.8 | 36.6 | 42.8 | 84.9 | -- | 90.4 | 69.7 | 41.8 | 85.6 | -- | 38.4 | -- | 32.6 | 53.9 | 55.0 |
| ProDA    | 87.8 | 45.7 | 84.6 | 37.1 | 0.6 | 44.0 | 54.6 | 37.0 | 88.1 | -- | 84.4 | 74.2 | 24.3 | 88.2 | -- | 51.1 | -- | 40.5 | 45.6 | 55.5 |
| DAFormer    | 84.5 | 40.7 | 88.4 | 41.5 | 6.5  | 50.0 | 55.0 | 54.6 | 86.0 | -- | 89.8 | 73.2 | 48.2 | 87.2 | -- | 53.2 | -- | 53.9 | 61.7 | 60.9 |
| HRDA | 85.2 | 47.7 | **88.8**    | **49.5**    | 4.8 | 57.2 | 65.7 | 60.9 | 85.3 | -- | 92.9 | 79.4 | 52.8 | **89.0**    | -- | **64.7**    | -- | 63.9 | 64.9 | 65.8 |
| DAFormer + B3CT | 82.8 | 44.2 | 86.7 | 38.9 | 5.0 | 55.0 | 63.0 | 61.2 | 83.6 | -- | 82.9 | 76.3 | 47.9 | 88.0 | -- | 58.6 | -- | 61.8 | 65.3 | 62.6 ± 1.0 |
| HRDA + B3CT | 90.5 | 57.5 | 88.7 | 46.3 | **7.5**     | **57.7**    | **66.6**    | **63.3**    | **89.1**    | -- | **94.2**    | **80.5**    | **55.8**    | **89.0**    | -- | 54.4 | -- | **64.6**    | **65.7**    | **67.0** ± 0.4  |

Reviewer comment (Experiments):
According to Table 3, it seems that HRDA (73.8) has been taken as the baseline. Did the author try to apply the proposed method on other methods/baselines and what about the performances/gains? It is very interesting to investigate the generalization ability of the proposed method by testing it on other baselines.

Author response:
**Q4: Further comparison on different baseline/method.**

**A4:** Investigating the generalization ability of a model is of great importance. We applied the B3CT model using DAFormer as the backbone on two benchmarks, GTA5 $\rightarrow$ Cityscapes and Synthia $\rightarrow$ Cityscapes respectively. The experimental results are shown in the table below and updated in Tab. 5. B3CT achieves a 1.5 and 1.7 percent point improvement over the respective two benchmarks. This implies that B3CT has the flexibility to integrate with various Transformer-based models seamlessly. 

#### Further baseline comparison on GTA5 $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| DAFormer    | 95.7 | 70.2 | 89.4 | 53.5 | 48.1 | 49.6 | 55.8 | 59.4 | 89.9 | 47.9 | 92.5 | 72.2 | 44.7 | 92.3 | 74.5 | 78.2 | 65.1 | 55.9 | 61.8 | 68.3 |
| HRDA | 96.4 | 74.4 | 91.0 | **61.6**    | 51.5 | 57.1 | 63.9 | **69.3**    | 91.3 | 48.4 | 94.2 | 79.0 | 52.9 | 93.9 | **84.1**    | 85.7 | 75.9 | **63.9**    | 67.5 | 73.8 |
| DAFormer + B3CT | **96.5**    | **75.2**    | 90.7 | 60.6 | 45.5 | 57.5 | 63.7 | 68.6 | 90.9 | 48.4 | 91.9 | 76.7 | 49.2 | 92.1 | 59.5 | 68.3 | 64.6 | 58.7 | 67.9 | 69.8 ± 0.4 |
| HRDA + B3CT | **96.5**    | 75.0 | **91.2**    | 59.6 | **55.4**    | **58.5**    | **66.3**    | 69.1 | **91.9**    | **49.3**    | **94.4**    | **79.2**    | **55.3**    | **94.2**    | 83.2 | **88.7**    | **80.3**    | 63.7 | **68.4**    | **74.8** ± 0.3   |

#### Further baseline comparison on Synthia $\rightarrow$ Cityscapes
|| Road | S.walk | Build. | Wall | Fence | Pole | Tr.Light | Sign | Veget. | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | M.bike | Bike | mIoU |
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
| DAFormer    | 84.5 | 40.7 | 88.4 | 41.5 | 6.5  | 50.0 | 55.0 | 54.6 | 86.0 | -- | 89.8 | 73.2 | 48.2 | 87.2 | -- | 53.2 | -- | 53.9 | 61.7 | 60.9 |
| HRDA | 85.2 | 47.7 | **88.8**    | **49.5**    | 4.8 | 57.2 | 65.7 | 60.9 | 85.3 | -- | 92.9 | 79.4 | 52.8 | **89.0**    | -- | **64.7**    | -- | 63.9 | 64.9 | 65.8 |
| DAFormer + B3CT | 82.8 | 44.2 | 86.7 | 38.9 | 5.0 | 55.0 | 63.0 | 61.2 | 83.6 | -- | 82.9 | 76.3 | 47.9 | 88.0 | -- | 58.6 | -- | 61.8 | 65.3 | 62.6 ± 1.0 |
| HRDA + B3CT | 90.5 | 57.5 | 88.7 | 46.3 | **7.5**     | **57.7**    | **66.6**    | **63.3**    | **89.1**    | -- | **94.2**    | **80.5**    | **55.8**    | **89.0**    | -- | 54.4 | -- | **64.6**    | **65.7**    | **67.0** ± 0.4  |