Semantic video segmentation aims at generating a sequence of pixel-wise class label maps for consecutive frames in a video. A real-time solution to this task is challenging due to the stringent requirements of speed and space. Prevailing real-time methods can be grouped into two major categories: per-frame methods and warping-based methods. Per-frame methods decompose the video task into a stream of mutually independent image segmentation tasks. They usually reduce the resolution of input images [2, 14, 29] or adopt a lightweight CNN [8, 14, 15, 19, 23, 29, 32, 33] to meet the real-time demand. In light of the visual continuity between adjacent video frames, warping-based approaches [7, 11, 16, 28, 36] utilize some inter-frame motion estimation (e.g., optical flows [28, 36]
or motion vectors) to avoid redundant computations, propagating (or warping) the segmentation results from the previous frame to the current one.
Although existing warping-based methods significantly boost the inference speed by saving the computational time for a large number of redundant frames, their performance drops considerably due to the following limitations. 1) Every pixel in a non-key frame is estimated by motion flows from some pixels in the previous frame. Such estimation may work for continuous regions in a video, but the moving of a non-rigid object such as a human usually becomes severely deformed, as shown in Fig. 1(a), since a non-rigid object in a new frame can contain a considerable amount of new pixels which cannot be estimated from the previous frame. 2) Errors accumulatealong consecutive non-key frames, making the results of later frames almost unusable, as shown in Fig. 1(b). All in all, warping behaves like a runaway fierce creature. The key to the issue is to tame it — to take advantage of its acceleration and to keep it under control.
To deal with the issues above, we propose to introduce a lightweight correction stage after the warping stage, resulting in a novel framework, Tamed Warping Network (TWNet), which contains a novel architecture and two correction modules for every non-key frame. First of all, we propose the non-key-frame CNN (NKFC) to perform segmentation for non-key frames. As shown in Fig. 2
, NKFC fuses warped deep features from a previous frame with the features from its shallow layers, making itself fast and able to retain spatial details, the requisites to do the following correction. To alleviate the issue of deformation, we introduce a correction stage consisting of two modules. We design the context feature rectification (CFR) module to correct the warped features with the help of spatial details contained in NKFC. To help CFR focus on error-prone regions, we design the residual-guided attention (RGA) module to utilize theResidual Maps in the compressed domain. It is worth noting that TWNet alleviates error accumulation in that it corrects every non-key frame rather than indulging the warping without restriction.
We evaluate TWNet on the Cityscapes  and CamVid  datasets. Experimental results demonstrate that TWNet greatly improve the accuracy and robustness of warping-based models. Specifically, for the videos on Cityscapes, the accuracy (mIoU) significantly increases from to by adding the correction, and the speed edges down from 65.5 FPS to 61.8 FPS. Furthermore, we found the performance improvements of non-rigid categories such as “human” and “object” are even higher than 18 percentage points.
The main contributions of this work are summarized as follows:
We propose a novel framework, Tamed Warping Network (TWNet), to improve the performance of warping-based segmentation models by introducing a lightweight correction stage.
We propose two efficient correction modules, the Context Feature Rectification module as well as the Residual-Guided Attention module, to alleviate the non-rigid object deformation and error accumulation problems during feature warping.
Experimental results on Cityscapes and CamVid demonstrate that the proposed TWNet framework greatly improves the robustness and accuracy.
2 Related Work
Feature fusion in semantic segmentation. Semantic segmentation in an FCN manner  has achieved remarkable accuracy. Recently, high-quality models [5, 9, 31, 34] as well as high-speed lightweight approaches [14, 19, 29, 33] show the importance of feature fusion from different layers (or scales). Generally, shallow layers contain more low-level spatial details, while deep layers more contextual information. The combination of features from different layers significantly improve the accuracy.
, where lateral connections are used to fuse the low-level (spatial) and high-level (context) features. In comparison, our non-key-frame CNN only retains a small number of layers of the encoder to extracts low-level features and obtains high-level features by interior feature warping. Thus, the non-key-frame CNN saves the heavy computations of context feature extraction.
Warping-based video segmentation. In general, videos are of temporal continuity, i.e., consecutive video frames look similar, making it possible to perform inter-frame prediction. The process of mapping pixels from the previous frame to the current one according to a pixel-level motion map is called image warping (Fig. 3). Each point in the motion map is a two-dimensional vector, , representing the movement from the previous frame to the current one. Motion vectors and optical flows are two kinds of commonly used motion maps. In general, motion vectors, already contained in videos, are less precise than optical flows (e.g., TV-L1  FlowNet2.0  and PWC-Net ). However, it takes extra time to perform optical flow estimation.
Researchers have proposed many warping-based semantic video segmentation approaches [7, 11, 28, 36]. Gadde et al.  proposed to enhance the features of the current frame by adding it with the warped features from previous frames. Zhu et al. , Xu et al.  and Jain et al.  proposed to use feature warping for acceleration. They divided frames into two types, key frames and non-key frames. Key frames are sent to the CNN for segmentation, while non-key frame results are obtained by warping. These warping-based approaches efficiently speed up the inference phase since the computational cost of warping is much less than that of CNN. However, both accuracy and robustness of these methods deteriorate due to the following reasons. First, neither optical flows nor motion vectors can estimate the precise motion of all pixels. Thus, there always exist unavoidable biases (errors) between the warped features and our expected ones. Second, in the case of consecutive non-key frames, the errors accumulate fast, leading to unusable results. To address the error accumulation problem, Li et al.  and Xu et al.  proposed to adaptively select key frames by predicting the confidence score for each frame. Jain et al.  introduced bi-directional features warping to improve the accuracy. However, all these approaches lack the ability to correct warped features.
3 Preliminaries: Warping-Correction in Video Codecs
In this section, we describe the basics and the pipeline of warping-correctionin video codecs. Warping is an efficient operation for inter-frame prediction. Given the previous frame and the motion vectors of the current frame , we can estimate the current frame by image warping. The process is described as
For a particular location index in an image, the mapping function is given by
However, there always exist biases between the warped image and the real one, especially in some complicated scenes and for the non-rigid moving objects. In order to correct the biases in image space, modern video codecs (e.g., MPEG , H.264 ) add a correction step after image warping (Fig.3(a)). Specifically, the codec performs pixel-wise addition between the warped image and the residual map to make correction. Each point in is a three-dimensional vector, , which describes the color differences between the warped pixel and the expected one. The overall inter-frame prediction process is described as
The correction step effectively addresses the bias problem and the error accumulation problem in image space. Inspired by this, we propose to learn the residual term in feature space (Fig. 3(b)) to alleviate problems incurred by feature warping.
4 Tamed Warping Network
In this section, we introduce the Tamed Warping Network (TWNet). We first outline the overall framework. Next, we introduce our non-key-frame CNN architecture and the correction modules, i.e., the Context Feature Rectification (CFR) module as well as the Residual-Guided Attention (RGA) module. Finally, we present the implementation details.
The whole framework of Tamed Warping Network (TWNet) is illustrated in Fig. 4. In our framework, video frames are divided into key frames (I-frames) and non-key frames (P-frames). The key frames are sent to the key-frame CNN (an U-shape [17, 21] FCN) where the context features of a selected interior layer are sent (warped) to the next frame. For non-key frames, the warped features are first corrected by the Context Feature Rectification (CFR) and Residual-Guided Attention (RGA) modules and then fused with the spatial features of the current frame. Then, the resultant features are used to make prediction and also sent to the next frame. The components of TWNet are described as follows.
4.2 Basic Model: The Non-Key-Frame CNN
Warping can be applied in feature space (Fig. 3(b)) since the “conv” layers in FCN models preserve the position information. Given the features of the previous frame , we can use to predict the features of the current frame by
In this work, we propose the non-key-frame CNN architecture as the basic model to perform segmentation for non-key frames considering both efficiency and accuracy. For one thing, in order to speed up, it applies interior warping to obtain deep context features. For another, different from previous warping methods, the non-key-frame CNN preserves several shallow head layers to add spatial features. Then, the two kinds of features are fused by lateral connections as FPN  and U-Net . Meanwhile, it is worth noting that the spatial features are also provided to the correction modules as the input.
4.3 The Correction Stage
Inspired by this correction step in video codecs, we propose to add a correction step (right part of Fig. 3(b)) in our segmentation pipeline. The correction stage consists of two key modules. The Context Feature Rectification (CFR) module corrects the warped features, and the Residual-Guided Attention (RGA) module guides the learning of CFR.
Context Feature Rectification. Although the non-key-frame CNN can add spatial details by employing low-level features via lateral connections, still, the errors in context feature would accumulate during successive feature warping. Thus, we claim the warped context features themselves need to be corrected. Comparing the pipeline of video codecs with warping-based segmentation methods (Fig. 3), we find the main problem of previous methods is the lack of a correction stage, which leads to inaccurate predictions and the error accumulation issue.
Inspired by the correction step in video codecs, we introduce the lightweight Context Feature Rectification (CFR) module to explicitly correct the warped context features. We design the CFR module considering the following aspects. First, the contextual information of the warped features is generally correct, except for the edges of moving objects. Second, the low-level features contain the spatial information, such as “edge” and “shape”. Thus, we claim the shape information in low-level features can help to correct the context feature and we apply feature aggregation to perform this correction. CFR takes the warped context features as well as the spatial features of the current frame as the inputs and outputs the corrected context features , as shown in Fig. 5(a). Specifically, the CFR module adopts a single-layer network, , which takes the concatenation , as the input and outputs the residual in feature space, . The mathematical form of CFR is defined as
During the training of CFR, in addition to the commonly used softmax cross entropy loss for pixel-level classification, we propose to employ an L2 consistency loss to minimize the distance between the corrected context features and the features extracted by the per-frame CNN.
Residual-Guided Attention. To guide the learning of CFR and further improve its performance, we propose the Residual-Guided Attention (RGA) module.
In our TWNet, the motion vectors used for feature warping are the same as those used in image warping. Therefore, the biases/errors should appear in the same spatial regions in image space and feature space. Thus, the residual maps in image space can be used as prior information to guide the learning of residuals in feature space . To realize this guidance, we adopt a lightweight spatial attention module named RGA to let the CFR module focus more on the regions with higher responses in . Specifically, we first resize the residual map to the shape of the warped context features. Then, we calculate the spatial attention map using a single-layer feed-forward CNN as follows
Finally, we apply spatial attention by performing element-wise multiplication between and . Fig. 5(b) illustrates the detailed structure of RGA. After applying this module, we formulate the whole process of TWNet for non-key frames, defined as
4.4 Implementation Details
Motion extraction and key frame selection. In our TWNet, we utilize motion vectors as motion maps. Both motion vectors and residuals are already contained in compressed videos. Thus, it takes no extra time to extract them. As for key frame scheduling, we simply regard all I-frames as key frames and P-frames as non-key frames, where I/P-frames are the concepts in video compression. An I-frame (Intra-coded picture) is stored as a complete image, while a P-frame (Predicted picture) is stored by the corresponding motion vectors and residual. Following previous works of [24, 27], we choose MPEG-4 Part 2 (Simple Profile)  as the compression standard, where each group of picture (GOP) contains an I-frame followed by 11 P-frames.
Layer selection in the non-key-frame CNN. It is worth noting that the layer of feature map used for warping can be arbitrarily chosen. In general, if we choose a deeper layer, there will be less corresponding head and tail layers (Fig. 6
), since layers in the encode-decoder architecture are paired by lateral connections. Thus, we will obtain a faster while less accurate model. Practically, we can adjust this hyperparameter to strike a balance between speed and accuracy.
Training of TWNet. In general, the training of TWNet contains two main steps, i.e., the training of the per-frame CNN and the training of the non-key-frame CNN. The training of the per-frame CNN is similar to that of other image segmentation methods, where the softmax cross entropy loss and the regularization loss are used. Then, we fix all the parameters in the per-frame model (the key-frame CNN) and start to train the modules in the non-key-frame CNN. As mentioned in Sec. 4.3, we employ an additional L2 consistency loss to minimize the distance between the corrected features and the context features extracted from the key-frame CNN. The object function of this training step is defined as
where and are weights to balance different kinds of losses.
During inference, we leave out the consistency loss since only one path (i.e., either the key-frame CNN or non-key-frame CNN) is used.
In this section, we present the experimental results of TWNet on high-resolution videos. First, we introduce the experimental environment. Then, we perform ablation study to validate the effectiveness of the proposed the non-key-frame CNN, CFR, and RGA. Finally, we compare TWNet with the state-of-the-art semantic video segmentation methods.
5.1 Experimental Setup
Dataset. There are several commonly used datasets (e.g., Cityscapes , CamVid , COCO-Stuff , and ADE20K ) for semantic segmentation. We perform training and evaluation on the Cityscapes dataset  considering its high-resolution property and the requirements that the inputs should be video clips rather than images. Cityscapes contains 5000 high-resolution finely annotated images, which are divided into 2975, 500, and 1525 for training, validation and testing respectively. Each image is the frame of a video clip. The dataset contains 19 classes for training and evaluation.
We perform ablation study on the validation set of Cityscapes and compare the results of TWNet with the state-of-the-art methods. Also, we perform experiments on CamVid to demonstrate that our framework is generic to different datasets.
Training. The training of TWNet is divided into two steps, i.e., the training of per-frame CNN and the training of the non-key-frame CNN. To train the per-frame model, we use the 2975 fine-annotated training images (i.e., the frames in the video clips). We use MobileNet 
pre-trained on ImageNet as the encoder of the key-frame CNN, and use three cascaded lateral connections  as the decoder. We apply the Adam optimizer  to train for iterations with initial learning rate and batch size . We update the pre-trained parameters with times smaller learning rate. Weight decay is set to . Training data augmentations include mean extraction, random scaling between to , random horizontal flipping, and random cropping to (
for CamVid). We implement the model using Tensorflow 1.12 and perform training on a single GTX 1080 Ti GPU card.
After the training of per-frame CNN, the parameters in it are fixed and we start to train the non-key-frame CNN. In each training step of non-key-frame CNN, we first send a batch of the frames into the per-frame CNN to extract their context features. Then, we perform warping and correction for the corresponding frames and calculate the loss according to Eq. 8. Note that if the consistency loss is employed, the frames should also be sent to the per-frame model to calculate the context features. In this training phase, random cropping is not adopted since the warping operation may exceed the cropped boundary. We set training size to ( for CamVid) with batch size 4.
Evaluation. In the inference phase, we conduct all the experiments on video clips with the resolution of . During evaluation, the key frame is uniformly sampled from the to the frame in the video clip, and the prediction of the frame is used for evaluation. No testing augmentation (e.g., multi-scale or multi-crop) is adopted. The accuracy is measured by mean Intersection-Over-Union (mIoU), and the speed is measured by frames per second (FPS). Our models run on a server with an Intel Core i9-7920X CPU and a single NVIDIA GeForce GTX 1080 Ti GPU card.
5.2 Ablation Study
We start building TWNet from the training of the per-frame model. We adopt the commonly used lightweight CNN, MobileNetV1, as the encoder. Our per-frame model achieves mIoU at 35.5 FPS.
The non-key-frame CNN. As described in Sec. 4.2, in the non-key-frame CNN, the layer of feature maps can be arbitrarily chosen to balance the accuracy and speed. We choose three layers in the decoder as the context features respectively. Results are summarized in Table 1.
According the experimental results, fine-tuning (the second training step) can significantly improve the performance. This demonstrates that low-level spatial features are more discriminative in the non-key-frame CNN, possibly due to the fact that the warped context features are less reliable in the non-key-frame CNN, thus the model depends more on low-level spatial features.
CFR module and consistency loss. We propose the CFR module to correct the warped context features. As shown in Table 2, the CFR module is effective and efficient. Table 2 also demonstrates the effectiveness of consistency loss, , and the weight term, , a crucial hyper-parameter for the training of CFR. By default, we set to 10 in the following sections for better performance.
RGA module. We introduce the RGA module to further exploit the correlation between residuals in image space and feature space. Results are demonstrated in Table 3. As expected, the RGA module further improves the performance of TWNet since it guides the CFR module to pay more attention to error-prone regions. The qualitative results of TWNet on Cityscapes are shown in Fig. 7.
Category-level improvement. The IoU improvements for different categories are shown in Table 4. The IoUs of non-rigid objects (human, object and vehicle) are improved greatly because of the following reasons. Since the motions of these moving objects are hard to predict, thus, the warped results are inaccurate. Our correction modules correct the wrong predictions and thus improve the accuracy.
|NKFC (The non-key-frame CNN)||51.2 (+7.4)||65.5 (+8.8)||84.6 (+2.4)||89.6 (+3.0)||89.1 (+2.0)||94.0 (+2.4)||97.3 (+0.7)|
|NKFC + CFR||62.2 (+18.4)||75.2 (+18.5)||89.7 (+7.5)||91.3 (+4.7)||90.8 (+3.7)||94.2 (+2.6)||97.9 (+1.3)|
|NKFC + CFR + RGA||62.2 (+18.4)||76.1 (+19.4)||90.1 (+7.9)||91.1 (4.5)||91.0 (+3.9)||94.2 (+2.6)||98.0 (+1.4)|
|Model||train set||eval set||resolution||mIoU-pf||mIoU||FPS-pf||FPS||FPS norm||GPU|
|ICNet ||train||val||67.7||-||30.3||-||49.7||TITAN X(M)|
|ERFNet ||train||test||69.7||-||11.2||-||18.4||TITAN X(M)|
|SwiftNetRN-18 ||train||val||74.4||-||34.0||-||34.0||1080 Ti|
|DFF ||train||val||71.1||69.2||1.52||5.6||12.8||Tesla K40|
|DVSNet1 ||train||val||73.5||63.2||5.6||30.4||30.4||1080 Ti|
|DVSNet2 ||train||val||73.5||70.4||5.6||19.8||19.8||1080 Ti|
|Prop-mv ||train||val||75.2||61.7||1.3||7.6||9.6||Tesla K80|
|Interp-mv ||train||val||75.2||66.6||1.3||7.2||9.1||Tesla K80|
Error accumulation. We also conduct experiments to show that TWNet is able to alleviate the error accumulation problem during consecutive warping. Suppose denotes the frame-level interval between the initial key frame and the frame to be evaluated. We set to different values and evaluate the performance of TWNet and the non-key frame CNN without correction modules. Results in Fig. 8 show that the correction modules significantly alleviate the accuracy degradation and improve the robustness of the models. Meanwhile, the employment of CFR and RGA takes little extra time.
5.3 Comparison with Other Methods
We compare the proposed TWNet with other state-of-the-art semantic video segmentation models, as shown in Table 5. Note that the speed (FPS) values measured in different models are just listed for reference, since the experimental environments may vary a lot. All of our models run on the platform with CUDA 9.2, cuDNN 7.3 and Tensorflow 1.12, and we use the timeline tool in Tensorflow to measure the speed. Following the recent work of , we include the “FPS norm” value based on the GPU types of previous methods 111GPU Benchmark: www.techpowerup.com/gpu-specs. Results demonstrate that our TWNet achieves highest inference speed with comparable accuracy on inputs with resolution of . Also, the accuracy of TWNet decreases more slightly than other video-based methods. It is worth noting that we adopt the key-frame CNN to simplify our presentation. If a more delicate per-frame model were used, the performance of TWNet would be further improved.
5.4 Results on the CamVid Dataset
We also conduct experiments on the CamVid dataset, which contains 367, 100, 233 video clips for training, validation and testing. We apply the same configurations as those of Cityscapes except for the crop size. Results are shown in Table 6, showing that TWNet is universal for different datasets. For comparison, our per-frame model achieves mIoU at 103.5 FPS.
We present a novel framework TWNet for real-time high-resolution semantic video segmentation. Specifically, we use warping and employ the non-key-frame CNN architecture for acceleration. In order to alleviate the errors caused by feature warping, we propose two efficient modules, namely CFR and RGA, to correct the warped features by learning the feature-space residual. Experimental results demonstrate that our method is much more robust than warping-based approaches while keeps the high speed.
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