SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Additionally, we use the updated motion feature maps  $\{\hat{\mathbf{B}}_1,\hat{\mathbf{B}}_2,\hat{\mathbf{B}}_3\}$  to predict a pixel-wise flow  $\mathcal{F}^{PF}\in \mathbb{R}^{2\times \frac{H}{4}\times \frac{W}{4}}$  in a coarse-to-fine manner, following [12], which is used to refine the query-based flow.
To obtain the final flow map  $\mathcal{F}_n^{j\to k}\in \mathbb{R}^{2\times H\times W}$  for the  $n^{th}$  query, we apply a 2D convolutional layer to the concatenated  $\mathcal{F}_n^{QF}$  and  $\mathcal{F}^{PF}$ , and then upsample the flow map to the original resolution:
$$
\mathcal {F} _ {n} ^ {j \rightarrow k} = \operatorname {U p s a m p l e} \left(\operatorname {C o n v 2 D} \left(\left[ \mathcal {F} _ {n} ^ {Q F}; \mathcal {F} ^ {P F} \right]\right)\right). \tag {4}
$$
Remark. We use  $N$  flow queries, which are initialized with the learned per-segment queries  $\mathcal{Q}_{\mathcal{O}}^{k}$  from the key frame, to capture the motion for each mask predicted on the key frame. In contrast to existing optical flow estimation methods, which model pixel-to-pixel dense correspondence, we propose a novel query-based flow that incorporates segment-level motion information between adjacent frames, assisting more accurate mask propagation in VSS.
Datasets. We evaluate our method on two benchmark datasets: VSPW [42] and Cityscapes [9]. VSPW is the largest video semantic segmentation benchmark, consisting of 2,806 training clips (198,244 frames), 343 validation clips (24,502 frames), and 387 test clips (28,887 frames). Each video in VSPW has densely annotated frames at a rate of 15 frames per second, covering 124 semantic categories across both indoor and outdoor scenes. Additionally, we present results on the Cityscapes dataset, which provides annotations every 30 frames. This dataset serves as an additional evaluation to showcase the effectiveness of our proposed method.
Table 1: Performance comparisons with state-of-the-art methods on VSPW dataset. We report mean IoU (mIoU) and Weighted IoU (WIoU) for the performance evaluation and Video Consistency (VC) for the temporal consistency comparison. We measure FLOPs (G) for computational costs, averaging over a 15-frame clip with resolution of  $480 \times 853$ . Frame-per-second (FPS) is measured on a single NVIDIA V100 GPU with 3 repeated runs.
Methods	Backbone	mIoU↑	WIoU↑	mVC8↑	mVC16↑	GFLOPs ↓	Params(M)↓	FPS↑
DeepLab3+ [4]	R101	34.7	58.8	83.2	78.2	379.0	62.7	9.25
UperNet [60]	R101	36.5	58.6	82.6	76.1	403.6	83.2	16.05
PSPNet [75]	R101	36.5	58.1	84.2	79.6	401.8	70.5	13.84
OCRNet [69]	R101	36.7	59.2	84.0	79.0	361.7	58.1	14.39
ETC [38]	OCRNet	37.5	59.1	84.1	79.1	361.7	-	-
NetWarp [15]	OCRNet	37.5	58.9	84.0	79.0	1207	-	-
TCB [42]	R101	37.8	59.5	87.9	84.0	1692	-	-
Segformer [62]	MiT-B2	43.9	63.7	86.0	81.2	100.8	24.8	16.16
Segformer	MiT-B5	48.9	65.1	87.8	83.7	185.0	82.1	9.48
CFFM-VSS [53]	MiT-B2	44.9	64.9	89.8	85.8	143.2	26.5	10.08
CFFM-VSS	MiT-B5	49.3	65.8	90.8	87.1	413.5	85.5	4.58
MRCFA [54]	MiT-B2	45.3	64.7	90.3	86.2	127.9	27.3	10.7
MRCFA	MiT-B5	49.9	66.0	90.9	87.4	373.0	84.5	5.02
Mask2Former [7]	R50	38.5	60.2	81.3	76.4	110.6	44.0	19.4
Mask2Former	R101	39.3	60.1	82.5	77.6	141.3	63.0	16.90
Mask2Former	Swin-T	41.2	62.6	84.5	80.0	114.4	47.4	17.13
Mask2Former	Swin-S	42.1	63.1	84.7	79.3	152.2	68.9	14.52
Mask2Former	Swin-B	54.1	70.3	86.6	82.9	223.5	107.1	11.45
Mask2Former	Swin-L	56.1	70.8	87.6	84.1	402.7	215.1	8.41
MPVSS	R50	37.5	59.0	84.1	77.2	38.9	84.1	33.93
MPVSS	R101	38.8	59.0	84.8	79.6	45.1	103.1	32.38
MPVSS	Swin-T	39.9	62.0	85.9	80.4	39.7	114.0	32.86
MPVSS	Swin-S	40.4	62.0	86.0	80.7	47.3	108.0	30.61
MPVSS	Swin-B	52.6	68.4	89.5	85.9	61.5	147.0	27.38
MPVSS	Swin-L	53.9	69.1	89.6	85.8	97.3	255.4	23.22
Implementation details. By default, all experiments are trained with a batch size of 16 on 8 NVIDIA GPUs. Note that pre-training is important for the DETR-like segmentors. For VSPW dataset, we pretrain Mask2Former on the image semantic segmentation task, serving as the per-frame baseline. For Cityscapes, we adopt pretrained Mask2Former with ResNet [18] and Swin Transformer [39] backbones from [7]. For the motion encoder, we utilized the weights from FlowNet [12] encoder which is pre-trained on the synthetic Flying Chairs dataset; the motion decoder and flow head are randomly initialized. We freeze most parameters of the pretrained Mask2Former, fine-tuning only its classification and mask head, as well as the proposed flow module. We apply loss functions in Mask2Former, including a classification loss and a binary mask loss on the class embeddings and warped mask embeddings, respectively. All the models are trained with the AdamW optimizer [41] for a maximum of 90k iterations and the polynomial learning rate decay schedule [4] with an initial learning rate of 5e-5.
Compared methods. To validate the effectiveness of our proposed model, we include the following methods for study: Per-frame: Employing Mask2Former [7] to independently predict masks for each video frame. Optical flow: Warping mask predictions from key frames to non-key frames using optical flow estimated by a lightweight optical flow model, i.e., FlowNet [12]. Query-random: Implementing the proposed flow estimation method with randomly initialized flow queries. Query-learned: Utilizing the proposed flow estimation method using the learned queries  $\mathcal{Q}_{\mathcal{O}}^{k}$  as initialization. Query-for-PF: Using pixel-wise flow  $\mathcal{F}^{PF}$  which incorporates segment-level information from the learned flow queries for warping predictions.
Evaluation metrics. Following previous works, we use mean Intersection over Union (mIoU) at single-scale inference, and Weighted IoU (WIoU) to evaluate the segmentation performance. We also compare models in terms of their computational complexity with FLOPs to evaluate the efficiency of these VSS models. We calculate FLOPs for each single frame by averaging on a video clip with 15 frames and fix the image resolution of  $480 \times 853$  and  $1024 \times 2048$  for VSPW and Cityscapes, respectively. For VSPW dataset, we adopt video consistency (VC) [42] to evaluate the visual smoothness of the predicted segmentation maps across the temporal dimension.
Table 2: Performance comparisons with the state-of-the-art VSS methods on Cityscapes. We report mIoU for the semantic segmentation performance and FLOPs (G) for the computational cost comparison, which is averaged by a video clip of 15 images with a resolution of  $1024 \times 2048$ . Frame-per-second (FPS) is measured on a single NVIDIA V100 GPU with 3 repeated runs.
Methods	Backbone	mIoU	GFLOPs	Params(M)	FPS
FCN [40]	R101	76.6	2203.3	68.5	2.83
PSPNet [75]	R101	78.5	2048.9	67.9	2.88
DFF [82]	R101	68.7	100.8	-	-
DVSN [66]	R101	70.3	978.4	-	-
Accel [22]	R101	72.1	824.4	-	-
ETC [38]	R18	71.1	434.1	-	-
SegFormer [62]	MiT-B1	78.5	243.7	13.8	20.7
SegFormer	MiT-B5	82.4	1460.4	84.7	7.20
CFFM-VSS [53]	MiT-B0	74.0	80.7	4.6	15.79
CFFM-VSS	MiT-B1	75.1	158.7	15.4	11.71
MRCFA [54]	MiT-B0	72.8	77.5	4.2	16.55
MRCFA	MiT-B1	75.1	145	14.9	12.97
Mask2Former [7]	R50	79.4	529.9	44.0	6.58
Mask2Former	R101	80.1	685.5	63.0	5.68
Mask2Former	Swin-T	82.1	543.6	47.4	5.41
Mask2Former	Swin-S	82.6	730.1	68.7	4.31
Mask2Former	Swin-B	83.3	1057.0	107.0	3.26
Mask2Former	Swin-L	83.3	1911.3	215.0	2.11
Mask2Former-DFF	R101	77.1	457.4	101.7	7.14
Mask2Former-DFF	Swin-B	79.9	525.3	145.7	6.09
Mask2Former-Accel	R101+R50	78.9	594.8	145.7	5.78
Mask2Former-Accel	Swin-B+Swin-T	81.4	680.1	193.1	4.40
MPVSS	R50	78.4	173.2	84.1	13.43
MPVSS	R101	78.2	204.3	103.1	12.55
MPVSS	Swin-T	80.7	175.9	114.0	12.33
MPVSS	Swin-S	81.3	213.2	108.0	10.98
MPVSS	Swin-B	81.7	278.6	147.0	9.54
MPVSS	Swin-L	81.6	449.5	255.4	7.24
The comparisons on VSPW dataset are shown in Tab. 1. We also report the backbone used by each method and the computational complexity (GFOLPs), averaged over a video clip of 15 frames, with each frame of resolution  $480 \times 853$ . For our proposed models, we use 5 as the default key frame interval for comparison. We have the following analysis: 1) The proposed MPVSS achieves comparable accuracy with significantly reduced computational cost when compared to the strong baseline method, Mask2Former [7]. Specifically, compared to Mask2Former, our MPVSS reduces the computational cost in terms of FLOPs by 71.7G, 96.2G, 74.7G, 104.9G, 162.0G, and 305.4G on R50, R101, Swin-T, Swin-S, Swin-B, and Swin-L backbones, respectively, while only experiencing  $1.0\%$ ,  $0.5\%$ ,  $1.3\%$ ,  $1.7\%$ ,  $1.5\%$ , and  $2.2\%$  degradation in the mIoU score. These results demonstrate a promising trade-off between accuracy and efficiency of our framework. 2) Our models with different backbones perform on par with the state-of-the-art VSS methods on the VSPW dataset with much fewer FLOPs. Specifically, our MPVSS model with the Swin-L backbone achieves an impressive mIoU of  $53.9\%$ , surpassing CFFM-VSS with MiT-B5 backbone by  $4.6\%$ . Notably, our approach achieves this performance with only  $24\%$  of the computational cost in terms of FLOPs. 3) When considering the comparable computational cost, our model consistently outperforms other state-of-the-art methods. For instance, MPVSS with Swin-L backbone surpasses Segformer with MiT-B2 backbone by  $10\%$  mIoU, under the FLOPs around 3.5G. These compelling results highlight the exceptional performance and efficiency of our MPVSS against the compared VSS approaches. In terms of temporal consistency, our proposed MPVSS achieves comparable mVC scores when compared to approaches that explicitly exploit cross-frame temporal context [54] or incorporate temporal consistency constraints during training [38]. We provide an explanation of VC score and a discussion in the supplementary material.
We further evaluate our MPVSS on the Cityscapes dataset and achieve state-of-the-art results with relatively low computational complexity. Notably, the Cityscapes dataset only provides sparse anno-
Table 3: Ablation study on VSPW dataset.
(a) Effect of the proposed flow estimation method.
Backbone	Per-frame	Copy	Optical Flow	Ours
R50	38.5	35.7	36.1	37.5
R101	39.3	37.1	38.2	38.8
Swin-T	41.2	37.5	38.4	39.9
Swin-S	42.1	38.7	40.1	40.4
Swin-B	54.1	49.3	52.1	52.9
Swin-L	56.1	50.4	53.2	53.9
(b) Effect of each component.
Flow design	Params(M)	Swin-T	Swin-B
Optical Flow	38.68	38.4	52.1
Query-random	37.56	38.1	50.9
Query-learned	37.56	39.2	52.4
Query-for-PF	40.08	38.5	52.3
Query-based flow maps	40.08	39.9	52.9
tations for one out of every 30 frames in each video. Therefore, for a fair comparison with previous efficient VSS methods, we report the results following the same evaluation process and compute their average FLOPs per-frame over all video frames. Specifically, compared to Mask2Former, our MPVSS reduces the computational cost in terms of FLOPs by 0.36T, 0.48T, 0.37T, 0.52T, 0.78T and 1.5T on R50, R101, Swin-T, Swin-S, Swin-B, and Swin-L backbones, respectively, with only  $1.0\%$ ,  $1.9\%$ ,  $1.4\%$ ,  $1.3\%$ ,  $1.6\%$  and  $1.7\%$  degradation in the mIoU score. The strong accuracy-efficiency trade-offs demonstrate that our proposed framework also works on sparse annotated video data.
Effects of the query-based flow. We compare the performance between the proposed query-based flow and the traditional optical flow in Table 3a. Overall, our approach demonstrates superior performance compared with the models relying solely on optical flow for mask propagation from key frames to non-key frames. To be specific, the utilization of the proposed query-based flow for mask warping consistently boosts the performance by  $1.4\%$ ,  $0.6\%$ ,  $1.5\%$ ,  $0.3\%$ ,  $0.8\%$  and  $0.7\%$  in terms of mIoU on R50, R101, Swin-T, Swin-S, Swin-B and Swin-L, respectively, which leads to less degradation to the per-frame baseline compared to models using pixel-wise optical flow. Moreover, directly copying the mask predictions from the key frame leads to a clear performance drop.
Effects of each component. In Table 3, we verify the effects of each component design in our query-based flow. We conduct experiments on VSPW using Swin-T and Swin-B. The performance of Query-random on the two backbones is slightly inferior to using optical flow. This suggests that without per-segment query initialization, the query-based flow struggles to retrieve specific motion information from motion features for one-to-one mask warping from the key frame, resulting in suboptimal performance. Then, by using key-frame queries as the initialization for flow queries, we observe an improvement of  $1.1\%$  and  $1.5\%$  mIoU scores for Query-learned on Swin-T and Swin-B respectively, compared to Query-random. Furthermore, we develop a variant design Query-for-PF, which introduces additional performance gain by  $0.1\%$  and  $0.2\%$  compared to optical flow respectively. These results underscore two findings: 1) learning query-based flow for each segment requires the learned queries from the key frame, for extracting meaningful motion features for each segment; 2) integrating segment-level information benefits the learning of pixel-wise flow. Therefore, we incorporate a refinement step using the enhanced pixel-wise flow for producing each query-based flow, which results in the best performance on both backbones. The outcomes highlight that warping mask predictions using our query-based flow consistently outperforms the methods using traditional optical flow on VSS.
Effects of the key frame interval. In Fig. 3a and Fig. 3b, we show the mIoU scores and TFLOPs versus key frame interval for our MPVSS with different backbones, respectively. Overall, all the results achieve significant FLOPs reduction with decent accuracy drop, which smoothly trade in accuracy for computational resource and fit different application needs flexibly. In Fig. 3c, we present the performance of warping mask predictions using our query-based flow and traditional optical flow, as well as direct copying version, on Swin-T. We observe that, when the key frame interval increases from 2 to 10, the performance of the proposed query-based flow only slightly degraded by  $1.1\%$  and  $0.7\%$  in terms of mIoU and WIoU score, while the scores are decreased by  $2.63\%$  and  $2.96\%$  using optical flow. These results indicate that the proposed query-based flow is more robust to capture long-term temporal changing for videos. We provide comparisons on other backbones in the supplementary material.
(a) mIoU vs. Key frame interval with different backbones.
(b) TFLOPs vs. Key frame interval with different backbones.
(c) mIoU vs. Key frame interval of different warping method.
Figure 3: Effects of the key frame interval. We compare the trade-off between accuracy and efficiency on variant backbones and warping strategies with respect to key frame interval. The number of FLOPs (T) is calculated based on a clip of 15 frames with a resolution of  $480 \times 853$ .
In Fig. 4, we present two examples from VSPW dataset to visually compare the prediction quality of the per-frame baseline, the method using optical flow, and the proposed MPVSS. We observe that the proposed query-based flow exhibits superior performance in capturing the movement of small objects when compared to optical flow. Additionally, the proposed mask propagation framework demonstrates enhanced category consistency compared to the per-frame baseline. For instance, the per-frame baseline exhibits category inconsistency as each frame is independently predicted. This highlights the advantage of our proposed approach in maintaining category coherence across consecutive frames.
Figure 4: Qualitative results. We present two examples from VSPW dataset. In each example, we display a series of consecutive frames from left to right. From top to bottom: (a) the ground truth; (b) the predictions of per-frame baseline, (c) the predictions of method using optical flow; (d) the predictions of the proposed MPVSS.
In this paper, we have presented a simple yet effective mask propagation framework, dubbed MPVSS, for efficient VSS. Specifically, we have employed a strong query-based image segmentor to process key frames and generate accurate binary masks and class predictions. Then we have proposed to estimate specific flow maps for each segment-level mask prediction of the key frame. Finally, the mask predictions from key frames were subsequently warped to other non-key frames via the proposed query-based flow maps. Extensive experiments on VSPW and Cityscapes have demonstrated that our MPVSS achieves SOTA accuracy and efficiency trade-off. Future work could explore extending MPVSS to other video dense prediction tasks, e.g., video object and instance segmentation.
Limitations and broader impacts. Although our framework significantly reduces computational costs for processing videos, the new module leads to an increase in the number of parameters in the models. Additionally, there might be a need for fine-tuning the new module or optimizing hyperparameters to achieve the best performance. It can lead to increased carbon emissions, indicating the potential negative societal impact.
[21] Z. Huang, T. Zhang, W. Heng, B. Shi, and S. Zhou. Real-time intermediate flow estimation for video frame interpolation. In ECCV, pages 624-642. Springer, 2022.
[22] S. Jain, X. Wang, and J. E. Gonzalez. Accel: A corrective fusion network for efficient semantic segmentation on video. In CVPR, pages 8866-8875, 2019.
[23] L. Kong, B. Jiang, D. Luo, W. Chu, X. Huang, Y. Tai, C. Wang, and J. Yang. Ifrnet: Intermediate feature refine network for efficient frame interpolation. In CVPR, pages 1969-1978, 2022.
[24] L. Kong, C. Shen, and J. Yang. Fastflownet: A lightweight network for fast optical flow estimation. In ICRA, pages 10310-10316. IEEE, 2021.
[25] S.-P. Lee, S.-C. Chen, and W.-H. Peng. Gsvnet: Guided spatially-varying convolution for fast semantic segmentation on video. In ICME, pages 1-6. IEEE, 2021.
[26] P. Lei and S. Todorovic. Recurrent temporal deep field for semantic video labeling. In ECCV, pages 302-317. Springer, 2016.
[27] H. Li, P. Xiong, J. An, and L. Wang. Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180, 2018.
[28] J. Li, W. Wang, J. Chen, L. Niu, J. Si, C. Qian, and L. Zhang. Video semantic segmentation via sparse temporal transformer. In ACMMM, pages 59-68, 2021.
[29] X. Li, X. Li, L. Zhang, G. Cheng, J. Shi, Z. Lin, S. Tan, and Y. Tong. Improving semantic segmentation via decoupled body and edge supervision. In ECCV, pages 435-452. Springer, 2020.
[30] Y. Li, J. Shi, and D. Lin. Low-latency video semantic segmentation. In CVPR, pages 5997-6005, 2018.
[31] Z. Li, C.-Z. Lu, J. Qin, C.-L. Guo, and M.-M. Cheng. Towards an end-to-end framework for flow-guided video inpainting. In CVPR, pages 17562-17571, 2022.
[32] C. Lin, C. Xu, D. Luo, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, and Y. Fu. Learning salient boundary feature for anchor-free temporal action localization. In CVPR, pages 3320-3329, 2021.
[33] J. Lin, C. Gan, and S. Han. Tsm: Temporal shift module for efficient video understanding. In ICCV, pages 7083-7093, 2019.
[34] S. Lin, H. Xie, B. Wang, K. Yu, X. Chang, X. Liang, and G. Wang. Knowledge distillation via the target-aware transformer. In CVPR, pages 10915-10924, 2022.
[35] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie. Feature pyramid networks for object detection. In CVPR, pages 2117-2125, 2017.
[36] J. Liu, J. He, J. Zhang, J. S. Ren, and H. Li. Efficientfnc: Holistically-guided decoding for semantic segmentation. In ECCV, pages 1-17. Springer, 2020.
[37] S. Liu, C. Wang, R. Qian, H. Yu, R. Bao, and Y. Sun. Surveillance video parsing with single frame supervision. In CVPR, pages 413-421, 2017.
[38] Y. Liu, C. Shen, C. Yu, and J. Wang. Efficient semantic video segmentation with per-frame inference. In ECCV, pages 352-368, 2020.
[39] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 9992-10002, 2021.
[40] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, pages 3431-3440, 2015.
[41] I. Loshchilov and F. Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
[64] H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao, and A. Geiger. Unifying flow, stereo and depth estimation. arXiv preprint arXiv:2211.05783, 2022.
[65] J. Xu, S. De Mello, S. Liu, W. Byeon, T. Breuel, J. Kautz, and X. Wang. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, pages 18134-18144, 2022.
[66] Y.-S. Xu, T.-J. Fu, H.-K. Yang, and C.-Y. Lee. Dynamic video segmentation network. In CVPR, pages 6556-6565, 2018.
[67] M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang. Denseaspp for semantic segmentation in street scenes. In CVPR, pages 3684-3692, 2018.
[68] F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015.
[69] Y. Yuan, X. Chen, and J. Wang. Object-contextual representations for semantic segmentation. In ECCV. Springer, 2020.
[70] Y. Yuan, J. Xie, X. Chen, and J. Wang. Segfix: Model-agnostic boundary refinement for segmentation. In ECCV, pages 489–506. Springer, 2020.
[71] K. Zhang, J. Fu, and D. Liu. Flow-guided transformer for video inpainting. In ECCV, pages 74-90. Springer, 2022.
[72] K. Zhang, J. Fu, and D. Liu. Inertia-guided flow completion and style fusion for video inpainting. In CVPR, pages 5982-5991, 2022.
[73] Y. Zhang, S. Borse, H. Cai, Y. Wang, N. Bi, X. Jiang, and F. Porikli. Perceptual consistency in video segmentation. In WACV, pages 2564-2573, 2022.
[74] Y. Zhang, A. Robinson, M. Magnusson, and M. Felsberg. Flow-guided semi-supervised video object segmentation. arXiv preprint arXiv:2301.10492, 2023.
[75] H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. Pyramid scene parsing network. In CVPR, pages 2881-2890, 2017.
[76] H. Zhao, Y. Zhang, S. Liu, J. Shi, C. C. Loy, D. Lin, and J. Jia. Psanet: Point-wise spatial attention network for scene parsing. In ECCV, pages 267-283, 2018.
[77] M. Zhen, J. Wang, L. Zhou, S. Li, T. Shen, J. Shang, T. Fang, and L. Quan. Joint semantic segmentation and boundary detection using iterative pyramid contexts. In CVPR, pages 13666-13675, 2020.
[78] S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In CVPR, pages 6881-6890, 2021.
[79] Z. Zhong, Z. Q. Lin, R. Bidart, X. Hu, I. B. Daya, Z. Li, W.-S. Zheng, J. Li, and A. Wong. Squeeze-and-attention networks for semantic segmentation. In CVPR, pages 13065–13074, 2020.
[80] X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai. Deformable transformers for end-to-end object detection. In ICLR, pages 3-7, 2021.
[81] X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei. Flow-guided feature aggregation for video object detection. In ICCV, pages 408-417, 2017.
[82] X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei. Deep feature flow for video recognition. In CVPR, pages 2349–2358, 2017.