SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Fortunately, benefiting from the analysis in Sec. 3.3.1, the solution to the computational redundancy of the decoder is intuitive, that is, to reduce the repeated intensive computation of continuous regions in the decoder, especially at the high-resolution features. First, inspired by the sparse segmentation and spatial region classification of the trimap, we hypothesize that most computation inside the matting decoder should be used in the unknown alpha matting regions, instead of an equal distribution on the certain foreground/background regions of the whole image. To approximate such unknown regions, we compute the incoherent regions  $M_{\mathrm{inc}}$  following [27] using the low-resolution output mask  $M \in \{0,1\}_{16}^{N \times N}$  from the first decoder block, where  $N$  denotes the original input scale.
Incoherent regions are primarily found along object instance boundaries or high-frequency areas (blue grids of the sparse feature in Fig. 2), and the points inside the incoherent regions are considered to be uncertain in alpha estimation. The remaining regions outside of the incoherent regions can be skipped/reduced in computation. Since the features of this process are the lowest resolution among the 4 sets of features input by the decoder, we obtain the mask  $M_{\mathrm{inc}}$  at a low cost.
Then, we employ the detected mask  $M_{\mathrm{inc}}$  to transform the convolutions in high-resolution blocks into sparse-assisted binarized convolutions for efficient computation. Our designed masked sparse convolution is different from [27, 43] on performing pointwise attention for the whole incoherent regions, resulting in a more binarization-friendly [44] and computation-efficient design. Specifically, we transform the  $3 \times 3$  convolution in the up-sample block of the decoder into:
$$
\boldsymbol {S A B}: \quad \boldsymbol {o} = \operatorname {S A - B i C o n v} _ {3} (\boldsymbol {x}; \text {b i l i n e a r} ^ {k} \left(M _ {\text {i n c}}\right)) + \operatorname {B i C o n v} _ {1} (\boldsymbol {x}), \tag {8}
$$
where  $\mathrm{SA - BiConv}_3(\cdot ;M)$  represents the sparse-assisted binarized  $3\times 3$  convolution under the guidance of sparse mask  $M_{\mathrm{inc}}$ , in which the weight and sparse activation are binarized, and bilinear denotes a bilinear interpolation up-sampling operation, and the low-resolution sparse mask  $M_{\mathrm{inc}}$  is up-sampled by  $k$  times,  $k\in \{2,4,8,16\}$ . For this binarized convolution SA-BiConv, the implementation of sparse computation follows [45, 27] to skip  $M_{\mathrm{inc}}(x,y) = 0$  masked regions during inference. However, this does not change that the essence of this convolution is still a non-grouped  $3\times 3$  convolution, which retains binarization-friendliness in practice. Moreover, as in Eq. (8), besides the sparse-assisted binarized  $3\times 3$  convolution, we also apply one  $1\times 1$  binarized convolution layer to process extraction in the whole feature and fuse the output obtained to that of SA-BiConv to guide the finer predictions of continuous regions.
Our SAB first obtains a sparse binary mask prediction based on low-resolution features, which is then used to assist sparse binarization in the higher-resolution features of the decoder. As validated in the experiment section (Tab. 1), SAB greatly reduces the computation of the binarized video matting model by reducing computational FLOPs by  $30\%$  while maintaining the accuracy of video matting.
3.4 Training Pipeline of BiMatting
We present the training pipeline for our BiMatting model, which involves additional training steps and iterations to ensure complete convergence of the binarized video matting model, as compared to the training of the full-precision RVM counterpart [1]. The training pipeline of our BiMatting is comprised of two phases, namely the pre-training phase and the matting training phase.
Pre-training phase: In the pre-training phase, we trained the binarized SBB backbone on the ImageNet classification dataset for 200 epochs to obtain a well-pre-trained backbone. This pretraining phase made it easier to converge during the matting training phase. Moreover, since the direct binarization of the full-precision pre-trained model, such as MobileNetV3, can lead to almost crashing, we apply the full pre-training phase for all compared binarized video matting models.
Table 1: Ablation result of BiMatting on VM [10] dataset. We ablate SBB and SAB in BiMatting at the encoder and decoder, respectively. Binarized RVM with MobileNetV3 is used as the baseline.
Model	Method	#Bit	#FLOPs(G)	#Param(MB)	MAD	MSE	Grad	Conn	dtSSD
RVMmbv3	-	32	4.57	14.5	6.08	1.47	0.88	0.41	1.36
RVMmbv3	-	1	0.55	0.64	28.49	18.16	6.80	3.74	3.64
RVMsBB	SBB	1	0.57	0.67	14.81	7.63	3.16	1.70	2.70
RVMsAB	SAB	1	0.35	0.67	189.13	184.33	15.01	27.39	3.65
BiMatting (Ours)	SBB+SAB	1	0.37	0.67	12.82	6.65	2.97	1.44	2.69
Matting training phase: This phase is binarization-aware and built following [1], divided into 4 stages. Stage 1 involves training on the low-resolution VM dataset for 20 epochs without DGF, with  $T = 15$  frames for quick updates. The SBB backbone's learning rate is set as  $1e^{-4}$  and the rest as  $2e^{-4}$ . Additionally, the input resolution  $h, w$  is sampled independently from 256-512px for improving robustness. In Stage 2, the network is trained with  $T = 50$ , with halved learning rate and 2 more epochs to enable learning of long-term dependencies. In Stage 3, the DGF module is attached, and 1 epoch is trained on both low-resolution long and high-resolution short sequences from the VM dataset. The low-resolution pass is  $T = 40$ ,  $h$  and  $w$  are the same with stage 1 without DGF, while the high-resolution pass employs DGF with downsample factor  $s = 0.25$ ,  $\hat{T} = 6$ , and  $\hat{h}, \hat{w} \sim (1024, 2048)$ . The learning rate of DGF is  $2e^{-4}$ , and that of the rest is  $1e^{-5}$ . In Stage 4, the network is trained for 5 epochs on D646 and AIM, increasing the decoder's learning rate to  $5e^{-5}$ .
We extensively evaluate the accuracy and efficiency of our proposed BiMatting. We first ablate our method and illustrate the contributions of SBB and SAB on the VM [10] dataset. Then we compare BiMatting with binarized video matting models that utilize existing binarization techniques on VM [10], D646 [29], and AIM [30], where our designs excel and even outperform some full-precision video matting models. Concerning efficiency, BiMatting impressively reduces computational FLOPs and model size by  $11.2 \times$  and  $21.6 \times$ , respectively. On metrics, we evaluate  $\alpha$  using mean absolute difference (MAD), mean squared error (MSE), spatial gradient (Grad), and connectivity (Conn) for quality, and dtSSD for temporal coherence. We also measure pixels where  $\alpha > 0$  by MSE for  $F$  [1]. All stages of our experiments use batch size 4 splits across 4 Nvidia A100 GPUs.
Tab. 1 demonstrates that the binarized video matting baseline experiences a significant drop in performance across all accuracy metrics for VM data recall. Despite its  $22.7 \times$  parameter compression, the model only realizes an  $8.3 \times$  computational savings in the efficiency metric. Upon substituting the encoder with our SBB alone, the binary model's accuracy is considerably restored, affirming that the encoder is the principal performance bottleneck in the baseline. However, the decoder's implementation of the efficient SAB does not fully resolve the performance bottleneck, where the accuracy of the binarized model is perilously close to collapsing. By integrating both of our contributions, both accuracy and efficiency performance is substantially enhanced. Notably, in BiMatting, combining these two improvements can produce a remarkable enhancement in accuracy performance, underscoring the importance that the decoder should concentrate on less but crucial representations to heighten model performance by providing high-quality features.
4.2 Comparison Results
To create the comparison benchmark, we combine test samples from the VM, D646, and AIM datasets with 20 video backgrounds and 20 image backgrounds, following the settings in [1, 47]. Each test clip contains 100 frames where motion augmentation is applied to image samples. We compare our BiMatting model with other video matting models that have been binarized using existing methods. These binarization methods include classical BNN [41] and DoReFa [18], as well as state-of-the-art (SOTA) ReActNet [23] and ReCU [21], where the latter two are considered as best practices for generic binarization [44]. In addition, we have also included results from some full-precision video matting methods for comparisons, such as the RVM [1] with MobileNetV3 [48] backbone (oracle), DeepLabV3 [28], and background-based BGMv2 [10] with MobileNetV2 [49] backbone. To ensure
Table 2: Low-resolution comparison on VM, D646, and AIM datasets. **Bold** indicates the best performance among binarized video matting models and† indicates the results is crashed.
Dataset	Method	#Bit	#FLOPs(G)	#Param(MB)	Alpha	FG
MAD	MSE	Grad	Conn	dtSSD	MSE
VM	DeepLabV3	32	136.06	223.66	14.47	9.67	8.55	1.69	5.18	-
512×288	BGMv2	32	8.46	19.4	25.19	19.63	2.28	3.26	2.74	-
RVM (oracle)	32	4.57	14.5	6.08	1.47	0.88	0.41	1.36	-
RVM-BNN†	1	0.50	0.57	189.13	184.33	15.01	27.39	3.65	-
RVM-DoReFa	1	0.52	0.57	51.64	34.50	8.85	7.14	4.09	-
RVM-ReCU†	1	0.52	0.64	189.13	184.33	15.01	27.39	3.65	-
RVM-ReAct	1	0.55	0.64	28.49	18.16	6.80	3.74	3.64	-
BiMatting (Ours)	1	0.37	0.67	12.82	6.65	2.97	1.42	2.69	-
D646	DeepLabV3	32	241.89	223.66	24.50	20.1	20.30	6.41	4.51	-
512×512	BGMv2	32	16.48	19.4	43.62	38.84	5.41	11.32	3.08	2.60
RVM (oracle)	32	8.12	14.5	7.28	3.01	2.81	1.83	1.01	2.93
RVM-BNN†	1	0.88	0.57	281.20	276.85	25.26	73.59	1.08	6.95
RVM-DoReFa	1	0.92	0.57	133.63	116.69	17.09	35.08	2.58	6.97
RVM-ReCU†	1	0.92	0.64	281.20	276.85	25.26	73.59	1.08	6.95
RVM-ReAct	1	0.97	0.64	56.41	43.10	14.05	14.85	2.56	6.85
BiMatting (Ours)	1	0.66	0.67	32.74	24.48	9.34	8.62	2.21	5.86
AIM	DeepLabV3	32	241.89	223.66	29.64	23.78	20.17	7.71	4.32	-
512×512	BGMv2	32	16.48	19.4	44.61	39.08	5.54	11.60	2.69	3.31
RVM (oracle)	32	8.12	14.5	14.84	8.93	4.35	3.83	1.01	5.01
RVM-BNN†	1	0.88	0.57	327.02	321.15	23.80	85.55	0.75	7.84
RVM-DoReFa	1	0.92	0.57	129.29	107.79	17.31	34.18	2.62	7.85
RVM-ReCU†	1	0.92	0.64	327.02	321.15	23.80	85.55	0.75	7.84
RVM-ReAct	1	0.97	0.64	59.90	44.08	14.32	15.90	2.37	8.00
BiMatting (Ours)	1	0.66	0.67	35.17	26.53	9.42	9.24	1.82	7.00
Table 3: High-resolution comparison on VM, D646, and AIM datasets. * indicates using the officially released model directly [46].
Dataset	Method	#Bit	#FLOPs(G)	#Param(MB)	SAD	MSE	Grad	dtSSD
VM	RVM	32	4.15	14.5	6.57	1.93	10.55	1.90
1920×1080	BGMv2*	32	9.86	19.4	49.83	44.71	74.71	4.09
RVM-ReAct	1	0.53	0.64	31.60	20.29	34.28	4.08
BiMatting (Ours)	1	0.38	0.67	18.16	11.15	21.90	3.25
D646	RVM	32	8.37	14.5	8.67	4.28	30.06	1.64
2048×2048	BGMv2*	32	15.19	19.4	57.40	52.00	149.20	2.56
RVM-ReAct	1	1.07	0.64	57.38	42.14	71.24	3.03
BiMatting (Ours)	1	0.77	0.67	52.85	44.08	61.60	3.12
AIM	RVM	32	8.37	14.5	14.89	9.01	34.97	1.71
2048×2048	BGMv2*	32	15.19	19.4	45.76	38.75	124.06	2.02
RVM-ReAct	1	1.07	0.64	57.38	42.14	71.24	3.03
BiMatting (Ours)	1	0.77	0.67	48.27	38.37	61.72	2.80
a fair comparison, we apply the exact same training pipeline to all binarized video matting networks as we did to BiMatting, as explained in Sec. 3.4. As for the full-precision video matting model, we follow the results reported in previous studies [1], unless otherwise specified.
Tab. 2 presents a comparison of methods that use low-resolution input. The findings indicate that applying BNN and ReCU directly to full-precision RVM leads to completely collapsed results. This is surprising given that the latter is among the SOTA binary methods, highlighting that binarizing existing video matting architectures is not a straightforward task. In contrast, our BiMatting model performs significantly better than all existing binarization models across all datasets, which predicts alpha with higher accuracy and consistency, resulting in more coherent and accurate performance. Further details will be presented in Sec. 4.3 with comprehensive visualizations. Furthermore, BiMatting even outperforms some 32-bit full-precision models when using only 1-bit limit bit width. For instance, BiMatting surpasses BGMv2 on VM, D646, and AIM datasets, as well as DeepLabV3 on
(a) Frame matting comparison.
(b) Temporal coherence comparison.
Figure 4: We compare our BiMatting with the existing SOTA binarized RVM-ReActNet and 32-bit full-precision (32-bit) RVM models to demonstrate its excellent accuracy.
VM datasets. These results demonstrate the enormous potential of binarization for efficient video matting. Tab. 3 presents a comparison between our BiMatting approach and other methods using high-resolution datasets. Our method consistently outperforms existing binarization models and BGMv2 across multiple metrics, demonstrating the robustness of BiMatting under varying resolutions.
Moreover, BiMatting has demonstrated its high potential for video matting in efficiency with impressive accuracy. As Tab. 2 and Tab. 3 show, in comparison to the full-precision counterpart (RVM), BiMatting achieves a computational FLOPs savings of 12.4 times and parameter savings of 21.6 times, making it the most promising solution for edge deployment. Moreover, BiMatting outperforms existing binarization methods, which offer significant acceleration advantages with only a tiny parameter overhead, such as a 0.1M increase compared to RVM-BNN.
4.3 Visualization
Fig. 4 and Fig. 5 show qualitative comparisons of natural videos. Fig. 4 compares our BiMatting model with RVM-ReActNet (the
Figure 5: Alpha detail comparison.
current SOTA binarized video matting model) and the full-precision RVM (can be seen as the 32-bit counterpart of BiMatting). In general, BiMatting's performance is close to the full-precision counterpart with greatly reduced resource consumption. Specifically, in Fig. 4(a), we conduct experiments on videos from diverse video frames. Our results reveal that BiMatting is more robust against semantic errors. Furthermore, BiMatting outperforms the other two models in matting edge regions. Fig. 4(b) compares temporal coherence, where our BiMatting consistently produces superior results, while RVM-ReActNet produces different areas of error. In Fig. 5, a comparison is made among the alpha predictions of various methods. Observe that our method outperforms the SOTA binarized video matting model by more accurately predicting intricate details, such as individual strands of hair.
**Limitation.** Though significantly improved, our BiMatting is not yet on par with its full-precision counterpart, the 32-bit RVM, particularly when it comes to local details such as the hair ends which tend to get blurred, while also favoring simpler backgrounds that lead to accurate matting results.
Our proposed model, BiMatting, is an efficient and accurate solution that utilizes binarization to achieve real-time video matting on edge devices constrained by computational resources. In this paper, we address the primary bottlenecks of binarization by constructing shrinkable and dense topologies for the binarized encoder block to enhance representation and sparsifying the binarized units to reduce redundant decoder computation. Our exhaustive experiments demonstrate that the proposed BiMatting outperforms existing binarized video matting models by a significant margin while producing a comparable performance to the full-precision counterpart in visual quality. BiMatting achieves significant savings in computation and storage, making it an attractive solution for real-world resource-constrained scenarios such as online conferences and short-form video production.
Acknowledgement This work was supported by the National Natural Science Foundation of China (No. 62022009), the State Key Laboratory of Software Development Environment (SKLSDE-2022ZX-23).
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Supplementary Material: BiMatting: Efficient Video Matting via Binarization
6 Overview
We offer further information in this supplementary material. Section 7 elaborates on our network architecture, providing detailed insights. In Section 8, we present additional results for detailed comparison and also showcase visual examples of our composited matting data samples. We highlight that video results are included in our supplementary material. We encourage readers to refer to our provided video for extensive matting results comparisons.
7 Details of Network Architecture
For the encoder (Table 4) constructed by our proposed SBB, it operates on individual frames to extract feature maps at various spatial scales ranging from  $\frac{1}{2}$  to  $\frac{1}{16}$ . Within each SBB, the number of feature channels is either doubled or stays the same in the first sub-block, then doubled again in the second sub-block, and finally halved in the third sub-block. This design enables each SBB to carry out feature extraction in a channel dimension space that exceeds the input dimension. Meanwhile, the computation-dense structure guarantees the utilization of binarized convolutions to acquire high-quality features. While traditional full-precision MobileNetV3 backbones operate at  $\frac{1}{32}$  scale, we made modifications to the last block. Specifically, we utilize convolutions with a dilation rate of 2 and a stride of 1, following the design principles from [1]. Furthermore, the final feature map ( $\frac{1}{16}$  scale) is passed to the LR-ASPP module, which compresses it into 128 channels.
Module	Conv32bit	SBB_1	SBB_2	SBB_3
Sub-Module	Sub-SBB (3)	Sub-SBB (3)	Sub-SBB (1)	Sub-SBB (3)	Sub-SBB (3)	Sub-SBB (1)	Sub-SBB (2)	Sub-SBB (3)	Sub-SBB (1)
In/Out Channel	(3, 16)	(16, 32)	(32, 64)	(64, 32)	(32, 64)	(64, 128)	(128, 64)	(64, 64)	(64, 128)	(128, 64)
Extracted Feature	1/2	1/4	1/8
Module	SBB_4	SBB_5	ASPP
Sub-Module	Sub-SBB (3)	Sub-SBB (3)	Sub-SBB (1)	Sub-SBB (2)	Sub-SBB (3)	Sub-SBB (1)	Sub-SBB (3)	Sub-SBB (3)
In/Out Channel	(64, 128)	(128, 256)	(256, 128)	(128, 128)	(128, 256)	(256, 128)	(128, 256)	(256, 1024)	(1024, 128)
Extracted Feature	1/16
For the decoder (Table 5), as mentioned in our paper, the SAB is employed in every decoder block except the first one to accelerate computations. The binary mask used by the SABs is obtained from the first non-sparse binarized block, which has the smallest feature scale and acquires the mask at a minimal cost. This design significantly improves the efficiency of the decoder.
Table 4: The details in the encoder of our BiMatting, where "Feature Scale" indicates the scale of features extracted by this sub-block that is utilized by the decoder. Sub-SBB (1), (2), and (3) follow the notations of Eq. (5) and (6) in our paper to represent different types of sub-blocks.
Module	BottleNeck	SAB_1 (Upsampling)	SAB_2 (Upsampling)	SAB_3 (Upsampling)	SAB_4 (Output)
Feature Scale	1/16	1/8	1/4	1/2	1/1
Input Mask	Minc	Minc	Minc	Minc
Produced Mask	Minc
Table 5: The details in the decoder of our BiMatting, where "Extracted Feature" indicates that the features extracted by this sub-block are utilized by the decoder. Sub-SBB (1), (2), and (3) follow the notations of Eq. (5) and (6) in our paper to represent different types of sub-blocks.  $M_{\mathrm{inc}}$  is the sparse mask to guide the decoder computation mainly in "difficult" regions.
Figure 6: More visual results. Compared to 1-bit video matting models using existing binarization methods, our BiMatting significantly surpasses them and achieves near full-precision performance. Note that the results of RVM-BNN indicate the model fully crashes.
For other parts, the Deep Guided Filter (DGF) incorporates a limited number of binarized  $1 \times 1$  convolutions internally. For more detailed specifications, please refer to [40, 1]. The complete network is constructed and trained using PyTorch [50].
8 Additional Visualizations
8.1 Visual Results
We show more visual results in Fig. 6, where we can more clearly see the advantages of our BiMatting over other binarization methods, both in edge details and local region matting. At the same time, we also provide a video (BiMatting.mp4 file in the supplementary material) to show the advantages of our BiMatting in more detail.
8.2 Composed Datasets
We follow [1] as a guide to constructing composite training and test samples. We show some examples of composited training samples from the matting datasets in Fig. 7. The clips contain natural movements when compositing with videos as well as artificial movements generated by the motion augmentation. Motion augmentation was exclusively applied to the foreground and background of the image in the testing samples (Fig. 8). The motion augmentation solely involved affine transforms. Moreover, the strength of the augmentation was deliberately toned down in comparison to the training augmentation, ensuring that testing samples possess a high-degree realism.
Figure 7: Composite training samples. The last column is the pixels' temporal standard deviation.
Figure 8: Example testing samples.