Title: UNSUPERVISED CONVOLUTIONAL NEURAL NET-WORKS FOR ACCURATE VIDEO FRAME INTERPOLA-TION WITH INTEGRATION OF MOTION COMPONENTS

Abstract: Optical flow and video frame interpolation are considered as a chicken-egg problem such that one problem affects the other and vice versa. This paper presents a deep neural network that integrates the flow network into the frame interpolation problem, with end-to-end learning. The proposed approach exploits the relationship between the two problems for quality enhancement of interpolation frames. Unlike recent convolutional neural networks, the proposed approach learns motions from natural video frames without graphical ground truth flows for training. This makes the network learn from extensive data and improve the performance. The motion information from the flow network guides interpolator networks to be trained to synthesize the interpolated frame accurately from motion scenarios. In addition, diverse datasets to cover various challenging cases that previous interpolations usually fail in is used for comparison. In all experimental datasets, the proposed network achieves better performance than state-of-art CNN based interpolations. With Middebury benchmark, compared with the top-ranked algorithm, the proposed network reduces an average interpolation error by about 9.3%. The proposed interpolation is ranked the 1st in Standard Deviation (SD) interpolation error, the 2nd in Average Interpolation Error among over 150 algorithms listed in the Middlebury interpolation benchmark.

1. INTRODUCTION:

Video frame interpolation is a classic video processing problem and is important for applications like frame rate up conversion and slow motion playback. Traditional algorithms for frame interpolation generally consist of two steps: motion estimation (ME) and motion-compensated frame interpolation (MCFI). ME estimates the motion trajectories between consecutive frames and MCFI generates interpolated frames by using the motion trajectories. The image quality of an interpolated frame depends on the accuracy of the motion trajectories. In the ME step, most algorithms generate dense motion fields between two consecutive frames using block matching (Bartels & Haan (2010) , Wang et al. (2010b) , and Nguyen & Lee (2017a)) or optical flow algorithms (Zach et al. (2007) , Horn & Schunck (1980) ). It is difficult to obtain accurate motion vectors for real-world sequences due to several challenges such as occlusion, reveal, fast and complex motions. In the MCFI step (Nguyen & Lee (2017b) and Wang et al. (2010a) ), the interpolation based on linear time-scale assumption often generates overlapped pixels and hole (missing pixels) in the interpolated frame even with accurate motion vectors.

Recently, a deep neural network is applied to frame interpolation with end-to-end learning approach. Starting from the work by Long et al. (2016) which employs an encoder-decoder (or auto-encoder) network, a number of recently-proposed deep networks successfully improves the quality of frame interpolation. The auto-encoder architecture or U-net architecture used in Niklaus et al. (2017b) and Liu et al. (2017) extracts features that are given to the sub-nets for the synthesis of the intermediate frame. SepConv network in Niklaus et al. (2017b) successfully handles blurry artifacts thanks to the independent estimation of four 1D kernels which are then convolved with the input frames to produce the interpolated frame. The SepConv network does not take into consideration of the smoothness among neighboring kernels because the kernels for each pixel are learned independently from those of neighboring pixels. A deep neural network is also used to directly estimate the phase decomposition of the intermediate frame in Meyer et al. (2018) based on the application of the phase based frame interpolation which is originally proposed by Meyer et al. (2015) to generate intermediate frames by modifying per-pixel phase.

In this paper, different from CNN based previous methods, the proposed method integrates a motion estimation component into the end-to-end learning framework. The proposed network integrates an optical flow estimation network as a driver for a motion-guided interpolator to complement original end-to-end network. The proposed network contains two interpolator sub-networks and a flow subnetwork that learns motions in order to drive the interpolator sub-networks to generate accurate intermediate pixels. The interpolator networks and flow network help each other to learn efficiently. The motion branch roles as a feedback or a smoothness constraint for the first end-to-end learning interpolator network. Meanwhile inputs for the motion branch are supplied by the first interpolator network. Therefore, the coefficients of the first interpolator are learned with two loss functions by both branches, the end-to-end learning one and the motion branch. In other words, it learns not only for pixel matching but also for the motion smoothness constraint. The proposed method integrates the flow network that blends the smoothness constraint into the frame interpolation problem, with end-to-end learning. In summary, the main contributions of the paper are

• An end-to-end unsupervised learning with the smoothness constraint for video frame interpolation.

• Highly accurate frame interpolation network that integrates motion information estimated by a flow estimation network as a driver for the motion-guided frame interpolator.

• A standard test dataset for various challenging cases in frame interpolation.

Experimental results show that the proposed network effectively deals with very challenging cases and produces higher visual quality results than state-of-art networks do. For Middlebury frame interpolation benchmark, the proposed network generates the best results among all the published ones, especially for real scenes.

The rest of the paper is organized as follows. Section 2 reviews previous related works. The proposed network is described in Section 3 and experimental results are presented in Section 4. Section 5 concludes this paper.

2. FRAME INTERPOLATION WITH CONVOLUTIONAL NEURAL NETWORKS:

A new approach using deep neural network makes promising progress in frame interpolation (Long et al. (2016) , Niklaus et al. (2017a) , Niklaus et al. (2017b), and Liu et al. (2017) ). A convolutional neural network (CNN) based on encoder-decoder architecture or U-net architecture estimates spatially-adaptive convolution kernels for every output pixel and convolve the kernels with the input frames for generating of an intermediate frame. The convolution kernels jointly represent the two de-coupled steps, motion estimation and image synthesis involved in traditional frame interpolation. The U-net architecture becomes a baseline for several CNNs. Long et al. (2016) , Niklaus et al. (2017b), and Liu et al. (2017) propose to use the U-net architecture for frame interpolation.

Another approach for frame interpolation attempts to use flow networks by taking advantage of improvement of optical flow. Super-Slomo network in Jiang et al. (2018) is proposed to use two U-net networks: the first network to estimate the initial optical flow between two original frames and the second one to refine and convert the motions between two original frames into the motions between the intermediate frames to two original frames. This idea is similar to the mapping method from classical FRUC proposed by Nguyen & Lee (2017b). The difference here is that the conversion is implemented by a deep neural network with U-net architecture, instead of the classical mapping. Therefore, it improves performance at motion boundaries as claimed by Jiang et al. (2018) . Xue et al. (2017) propose to use two networks for video frame interpolation: the first network for motion estimation and the second one to generate an interpolated frame. CtxSyn in Niklaus & Liu (2018) is another method that employs a state of art optical flow estimation in Sun et al. (2018) in order to generate highly accurate frame interpolation. Optical flow is estimated from original frames and is later used for spatial warping operations in order to provide inputs for a grid architecture network that synthesizes the interpolated frame from the stacked warped images. This network shows the importance of both the accurate estimation of the optical flow and efficient synthesis algorithm. SepConv inNiklaus et al. ( 2017b) uses four sub-nets in addition to a typical encoder-decoder or Unet architecture in order to train horizontal and vertical filters separately. Therefore it reduces blur artifacts that still exists in the interpolated frames generated by the previous typical U-net based methods (Long et al. (2016) and Liu et al. (2017) ). Both Interpolator network and Flow network share the same architecture that is an encoder-decoder network with skip connections, called Core Network. The only difference between two networks is in the final layer. In Interpolator networks like the final layer of SepConv [23] , four sub-nets are used to generate four kernel coefficients that implicate the motion information and reduce blurry artifacts caused by direct synthesis with the same architecture. In Flow network, the final layer only uses one sub-net to estimate the flow value.

3. CONVOLUTIONAL NEURAL NETWORKS WITH MOTION INTERGRATION FOR FRAME INTERPOLATION:



3.1.1. CORE NETWORK:

The core network is a fully-convolutional encoder-decoder architecture which is the main component for both of Interpolator Networks and Flow Network. It consists of five hierarchies of convolution layers, four hierarchies of deconvolution layers and four skip connections. A basic block contains three convolution layers followed respectively by three rectified linear units (ReLU) applied for both an encoder and a decoder. The convolution kernel size is 3x3. For the encoder of the network, a pair of basic blocks and an average-pooling layer with the pooling size and pooling stride equal to 2 is used to down-sample the input features with a down-scale factor of 2. For the decoder, each processing unit consists of the basic block, bilinear up-sampling and a convolution layer. To maintain spatial information, skip connections are added between the corresponding convolution and deconvolution layers at the same spatial resolution. The corresponding deconvolution layers and convolution layers are concatenated together before being fed forward.

3.1.2. INTERPOLATOR NETWORKS:

Similar to SepConv network, four sub-nets are followed by the core network to learn two pairs of 1D kernels: horizontal and vertical kernels. For fair comparison with SepConv Network, the size of the 1D kernel is chosen to 51 pixels for each kernel which is the same as that in SepConv Network.

The input frames are convolved with kernels to generate the interpolated frame as the following equation.

EQUATION

where P 1 (x, y) and P 2 (x, y) are the patches centered at (x, y) in I 1 and I 2 . The pixel-dependent kernels K h 1 (x, y), K v 1 (x, y), K h 2 (x, y), and K h 2 (x, y) encode both motion and sampling information from two original patches.

3.1.3. MOTION DERIVATION LAYERS:

The coefficients of the above 1D kernels implicate motion information and they are exploited to derive the flow information. The motions are encoded as the offsets of the non-zero kernel values to the kernel center. The motion vector is the weighted sum of the offsets. Therefore, the values of the coefficients and the offsets are used in order to compute the motions. There are four 1D kernels, two corresponding to the displacement of frame 1 to the interpolated frame, and the others corresponding to the displacement of frame 2 to the interpolated frame. Therefore, the optical flows for both the forward and backward directions with point of view from the intermediate frame are computed directly. Unlike the method in Jiang et al. (2018) , that estimates the intermediate bidirectional optical flows indirectly by approximating the original uni-directional flows; that produces artifacts and errors around motion boundaries. The formulations of the motion derivation layers are represented by following equations:

u 1.5->1 = 51 i=1 weight h1 i * of f set h1 i 51 i=1 weight h1 i (3) v 1.5->1 = 51 i=1 weight v1 i * of f set v1 i 51 i=1 weight v1 i (4) u 1.5->2 = 51 i=1 weight h2 i * of f set h2 i 51 i=1 weight h2 i (5) v 1.5->2 = 51 i=1 weight v2 i * of f set v2 i 51 i=1 weight v2 i (6)

where u 1.5->1 and v 1.5->1 are horizontal and vertical components of the flow from the intermediate frame to frame 1, u 1.5->2 and v 1.5->2 are horizontal and vertical components of the flow from the intermediate frame to frame 2. of f set h1 i , of f set v1 i , of f set h2 i , of f set v2 i are the displacements of the coefficients weight h1 i , weight v1 i , weight h2 i , weight v2 i to the center position in the corresponding 1D kernels.

3.1.4. FLOW NETWORK:

The input of the flow network is the initial optical flow obtained by the above motion derivation layers. However, those values are estimated separately for each pixel, based on kernel values of each pixel. There is no smoothness constraint between them. Therefore, the initial optical flows are quite noisy and sometimes inaccurate. The flow network works as a refinement process that blends the smoothness constraint into the raw initial optical flow of pixels through a convolution-deconvolution neural network. Similarly to the interpolator networks, the flow network is also an encoder-decoder network that contains two main components: the core network and a final layer that composes up bilinear up-sampling and convolution operations without rectified linear units.

3.1.5. FRAME WARPING:

Guided by the estimated optical flow, the proposed method warps the input frames into the intermediate timescale by using both forward and backward warping functions, which can be implemented via bilinear interpolation and are differentiable. Specifically, the proposed method employs forward warping that uses the refined optical flow to warp the input frame I 1 to the target locations in the intermediate frame and obtain a warped frame I 1.5-1 . The proposed method warps the input frame I 2 and generates warped frame I 1.5-2 in the same way by using backward warping. Two warped frames are the closest frames to the true interpolated frame. Therefore, they are very suitable for the inputs of Interpolator network 2 that works as a frame refinement to generate the intermediate frame. This step narrows down distances between two consecutive input frames and the intermediate one.

In addition, it is easier for the network to learn kernels when two inputs are closer. However, this approach may lead to holes or missing pixels in the warped outputs, mostly due to occlusion and reveal (Lu et al. (2018) ). In addition, the performance of warping operations depends on the accuracy of the estimated flow, and therefore, errors in optical flow cause a loss of contextual information from original frames. Therefore, in some cases the quality of warped pixels become worse than the original ones.

3.2. TRAINING:

Following Niklaus et al. (2017b) , the proposed neural network parameters are initialized by a convolution aware initialization (Aghajanyan (2017) and trained by using AdaMax (Kingma & Ba (2014)) with β 1 = 0.9, β 2 = 0.999, a learning rate of 0.001 and a mini-batch size of 16 samples. Training dataset: The proposed network uses the training dataset provided by Xue et al. (2017) . A high resolution dataset is chosen because the resolution of training dataset affects the quality of learned parameters. In addition, the size of images in the training dataset is a multiple of 32, that is suitable with proposed network architecture that contains down-sampling layers. For frame rate up conversion, the video sequences that are used for training should be not an up-rated video sequences. In addition, downloaded videos should be not compressed videos due to some compression artifacts. In addition, the histogram of motion in the dataset is diverse and the dataset contains both outdoor and indoor scenes. The video size for training are 448x256, are cropped from the full high resolution videos 1920x1080. This size is chosen because the cropped image has almost the same ratio between the width and height of the original. Furthermore, the height and width of the cropped image are multiples of 32, making them convenient down-sampling and up-sampling operations in the network. In addition, the patches of size 448x256 instead of training with entire full HD frames make it possible to avoid patches that contain no useful information and leads to diverse mini-batches as proposed by Bansal et al. (2017) to improves training efficiency. For data augmentation during training process, the trainer randomly swaps the temporal order between input frames, frame1 becomes frame2 and vice versa. This makes dataset larger and eliminates potential priors.

4. EXPERIMENTAL RESULTS:



4.1. FRAME INTERPOLATION EVALUATION:

The proposed network for frame interpolation is compared with a representative selection of state-ofthe-art methods in both quantitative and qualitative manners with various datasets including a wellknown benchmark, as well as a new challenging dataset proposed in this paper to include difficult 2012) is chosen as a representative of optical flow which performs the second best interpolation with the Middlebury benchmark. To synthesize interpolated frames from the computed optical, the same algorithm as in Baker et al. ( 2011) is employed. The performance of MDP-Flow2 is followed closely by a neural network based frame interpolation called SepConv in Niklaus et al. (2017b) , as well as a representative phased-based method in Meyer et al. (2015) are also compared. Two datasets are used for performance evaluation. The first one is the well-known Middlebury benchmark. The second one is a new dataset proposed in this paper to cover the difficult cases for frame interpolation. These cases include movement of text objects, occlusion, reveal, and movement of small fast objects. Movement of text objects as a subtitle and logos often is difficult for interpolation because the movement often takes place in a background and it is in a different direction from the background. Object occlusion and reveal are difficult in a classical computer vision problem such as optical flow and they are also difficult in frame interpolation. A small object is difficult to estimate its motion and so is fast and complex movement in a video. This new dataset is used to measure the performance of frame interpolation algorithms that focus on enhancement of visual quality. For explanation, this new data set is called Hard Cases for Display (HCD) which consists of six high definition video sequences with hard and challenging cases for frame interpolations such as scenes with sub-title, occlusion and reveals, halo artifacts and fast complex motions. Three sub-title sequences, denoted by Sub1, Sub2, and Sub3 are digital broadcasting videos with sub-titles displayed on background regions. Two occlusion and reveal sequences are denoted by Occlusion1 and Occlusion2, respectively. Occlusion1 sequence contains scenes at an inter-section of a crowed street with several layers of objects in which one layer occludes the others because of moving objects. Occlusion2 sequence is captured when a person suddenly enters the scene and later he is occluded by a wall. The last sequence captures a soccer match where the movement of players is fast and complex and the ball is a small object.

Table 1 shows the average interpolation error (AIE) as defined in Baker et al. (2011) where the interpolation error is the root-mean-square (RMS) difference between the ground-truth image and the estimated interpolated image. The proposed network outperforms state-of-art methods (Niklaus & Liu (2018) , Xu et al. (2012) , Jiang et al. (2018) , Niklaus et al. (2017b), and Weinzaepfel et al. (2013) ) and improves the best previous method by a significant margin. Especially with Backyard, Basketball and Evergreen datasets which show real-world scenes, captured with a real camera and containing real sources of noise, the proposed network is consistently the best by notable margins. The proposed interpolation is ranked the 1st in Standard Deviation (SD) interpolation error, the 2nd in Average Interpolation Error among over 150 algorithms listed in the benchmark website.

Table 2 show quantitative comparisons among the proposed methods with representative state of art methods with HCD dataset. In both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), the proposed method outperforms the representative state of art methods with notable margins. Figure 2 shows the interpolated frames for subjective quality comparison. In the top rows of Figure 2 , the text objects in the sub-title include artifacts still exist with the previous methods that are based on optical flow estimation, the phased based method and deep neural network, SepConv [23] . Meanwhile, the proposed method successfully removes these artifacts. In occlusion dataset, as shown in the middle rows in Figure 3 , the area surrounding a moving car occludes a background area when the car is moving. There are blurry artifacts in interpolated frames generated by Phase-based method (Meyer et al. (2015) and SepConv network (Niklaus et al. (2017b) ) and Salt and Pepper artifact in MDP-Flow2 (Xu et al. (2012) due to pixels in an occlusion area caused by wrong optical flow. Meanwhile, both blurry and Salt and Pepper artifacts are alleviated by the proposed method.

In fast and complex motion dataset as shown in the bottom rows of Figure 3 , the movement of the leg of the soccer player and that of the hand of the goalkeeper is fast and complex. The proposed method improves significantly visual quality in comparison with the previous methods. Table 3 shows performance comparisons between the two loss functions and the only second term of the loss function.As reported in the table, the use of two loss functions outperforms that of only the second loss function. Because the first term in loss function helps to estimates kernels correctly, thus derived motion vectors are estimated better than the case without the first term. In other words, the first term roles as a correction for motion derivation layers of the second branch. In addition, with two loss functions, Interpolator network 1 is learned deeper, by both branches of the networks with two combinative targets, pixel matching and smoothness constrains on motion fields. Thus, it is updated by both loss functions and trained with both end-to-end framework and motion based learning.

Flow Net: The second branch of the proposed method includes Flow net that refines an initial flow that is encoded in kernels' coefficients of Interpolator network 1. The performance of Flow net is compared with state of art optical flow (Xu et al. (2012) ), the warping operations and Interpolator network 2 are also used to generate the intermediate frame for fair comparison between both optical flow methods. Table 4 reports the performance of both approaches.

Frame Synthesis Network: Warping operations and the second interpolator network compose a new video frame synthesis method from estimated motions. In order to evaluate the contribution of the frame synthesis network, with the same optical flow generated by Xu et al. (2012) , the interpolated frame generated by the synthesis network outperforms the results obtained by the benchmark algorithm (Baker et al. (2011) ) as reported in Table 5 . This shows that the motion compensation or frame interpolation algorithm is able to be improved by neural networks.

5. CONCLUSION:

This paper proposes a convolutional neural network with motion integration for video frame interpolation or frame rate up conversion. The proposed method is an end-to-end learning approach. It adopts a motion estimation network as a driver for optical flow-guided Interpolator Networks in order to support end-to-end learning. The motion network branch provides another loss function to make the first Interpolator network learn deeper and more efficient. This alleviates errors caused by learned pixels' wrong kernel coefficients with only original one-time loss function. The interpolated frames by the proposed method are high-quality frames and outperform interpolation results obtained by state-of-the-art methods both quantitatively and qualitatively. The proposed interpolation

:



Figures and Tables:

Figure 1 shows the proposed CNN for frame interpolation which composes of two branch networks: the first branch network is an end-to-end learning, corresponding to the first term in the loss function and the second branch network is a motion guided interpolation, corresponding to the second term in the loss function. Both branch networks are connected via Motion derivation layers. Consequently, both branches help each other for efficient training. In addition, in the view point of the first branch, the second branch network roles as a smoothness term that integrates motion components into the end-to-end learning. Meanwhile Interpolator network 1 in the first branch generates the initial flows to be used as the input to Flow net in the second branch. This leads the Flow net starts to learn motions from a quite good initialization values, not from scratch. Thus, more accurate flows are estimated by the Flow net in the second branch. Two consecutive original frames are used as the inputs to Interpolator network 1. From the kernels estimated by Interpolator network 1, the initial motion vectors are derived by Motion derivation layers. The output of Motion derivation layers are used as the input for Flow Network which refines the initial optical flows. Flow network adopts a U-net or auto-encoder architecture with skip layers for efficient learning. With the refined optical flows as the output of the Flow Network, warping operations map the original frames into the target locations in the temporal intermediate frames with blending motion constraints and scenarios into the network. These intermediate frames are used as the inputs for Interpolator network 2 to generate a new intermediate frame in the second branch.

Figure 1: Diagram of the proposed method

Figure 2: Visual Comparison of Interpolated Frames on HCD Dataset

Evaluation on Middebury benchmark Average Mequon Schef. Urban Teddy Backy. Basket. Dump. Ever.

Table content: <table><tr><td>Proposed</td><td>4.79</td><td>2.66</td><td>3.37</td><td>3.23</td><td>4.87</td><td>7.51</td><td>4.59</td><td>5.87</td><td>6.18</td></tr><tr><td>CtxSyn</td><td>5.28</td><td>2.24</td><td>2.96</td><td>4.32</td><td>4.21</td><td>9.59</td><td>5.22</td><td>7.02</td><td>6.66</td></tr><tr><td>MDP-Flow2</td><td>5.83</td><td>2.89</td><td>3.47</td><td>3.66</td><td>5.20</td><td>10.2</td><td>6.13</td><td>7.36</td><td>7.75</td></tr><tr><td>SuperSlomo</td><td>5.31</td><td>2.51</td><td>3.66</td><td>2.91</td><td>5.05</td><td>9.56</td><td>5.37</td><td>6.69</td><td>6.73</td></tr><tr><td>SepConv</td><td>5.61</td><td>2.52</td><td>3.56</td><td>4.17</td><td>5.41</td><td>10.2</td><td>5.47</td><td>6.88</td><td>6.63</td></tr><tr><td>DeepFlow</td><td>5.97</td><td>2.98</td><td>3.88</td><td>3.62</td><td>5.39</td><td>11.0</td><td>5.91</td><td>7.14</td><td>7.80</td></tr><tr><td colspan="5">cases for frame interpolation. MDP-Flow2 in Xu et al. (</td><td/><td/><td/><td/><td/></tr></table>

Objective comparisons on HCD dataset

Table content: <table><tr><td>Sequences</td><td colspan="2">MDPFlow2</td><td colspan="2">Phase-based</td><td colspan="2">SepConv</td><td colspan="2">Proposed</td></tr><tr><td/><td colspan="8">PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM</td></tr><tr><td>Subtitle 1</td><td>33.98</td><td>0.987</td><td>29.97</td><td>0.976</td><td>34.26</td><td>0.988</td><td>35.00</td><td>0.989</td></tr><tr><td>Subtitle 2</td><td>32.84</td><td>0.992</td><td>26.14</td><td>0.969</td><td>33.12</td><td>0.992</td><td>34.23</td><td>0.992</td></tr><tr><td>Subtitle 3</td><td>36.71</td><td>0.990</td><td>34.64</td><td>0.985</td><td>37.18</td><td>0.991</td><td>37.11</td><td>0.991</td></tr><tr><td colspan="2">Occlusion 1 30.80</td><td>0.963</td><td>24.13</td><td>0.831</td><td>32.60</td><td>0.974</td><td>33.24</td><td>0.975</td></tr><tr><td colspan="2">Occlusion 2 41.16</td><td>0.990</td><td>39.47</td><td>0.985</td><td>42.67</td><td>0.992</td><td>42.73</td><td>0.991</td></tr><tr><td>Halo</td><td>29.38</td><td>0.960</td><td>24.87</td><td>0.866</td><td>29.76</td><td>0.964</td><td>31.25</td><td>0.971</td></tr><tr><td>Average</td><td colspan="8">34.15 0.9803 29.87 0.9353 34.93 0.9835 35.59 0.9848</td></tr></table>

Peer review (iclr2019):
 The paper is not well organized and contains many typos or mistakes. The claimed contributions are ambiguous, especially the “unsupervised” learning, and “smoothness constraint”. The proposed method should be evaluated on larger benchmark datasets, e.g., the UCF101 and Vimeo90K datasets, which are commonly used in the state-of-the-art algorithms. The ablation studies are insufficient by only evaluation on the proposed HCD dataset. Clarity: Moderately clear. Authors need to polish the presentation and term (e.g., SD), and fix typos (e.g., state-of-the-art) throughout the paper. Originality: Minor novelty. The network takes the SepConv architecture as backbone for both the Flow Network and Interpolator Networks. The authorsThis paper aims to find important regions to classify an image. The main algorithm, FIDO, is trained to find a saliency map based on SSR or SDR objective functions. The main novelty of this work is that it uses generative models to in-fill masked out regions by SSR or SDR. As such, compared to existing algorithms, FIDO can synthesize more realistic samples to evaluate. I like the motivation of this paper since existing algorithms have clear limitations, i.e., using out-of-distribution samples. This issue can be addressed by using a generative network as described in this paper. However, I think this approach yields another limitation: the performance of the algorithm is bound by the generative network. For example, let’s assume that a head region is important to classify birds. Also assume that the proposed algorithm somehow predicts a mask for the head region during training. If the generative network synthesizes a realistic bird from the mask, then the proposed algorithm will learn that the head region is a supporting region of SSR. In the other case, however, the rendered bird is often not realistic and classified incorrectly. Then, the algorithm will seek for other regions. As a result, the proposed method interprets a classifier network conditioned on the generative network parameters. Authors did not discuss these issues importantly in the paper. Although the approach has its own limitation, I still believe that the overall direction of the paper is reasonable. It is because I agree that using a generative network to in-fill images to address the motivation of this paper is the best option we have at this current moment. In addition, authors report satisfactory amount of experimental results to support their claim. propose a flow derivation layer to transform convolution kernels to flows, which is the novel part. Significance: Moderately significant. The authors present good video frame interpolation results by inferring flow from estimated convolution kernels. Pros: 1. The interpolation performance on the Middlebury dataset is good. Especially on the 4 of the 8 compared sequences, the performance is the best with significant margins than the second best approaches. However, larger datasets, such as Vimeo90k and UCF101, should also be compared to understand the generalization of the proposed method. 2. This paper proposes an interesting combination of interpolator network and flow network. Since it is claimed that ”The interpolator networks and flow network help each other to learn efficiently”(Page2,Line9) and the two networks as a basic block can be repeatedly concatenated in the model (e.g., {interpolator, flow}->{interpolator, flow}-> ... ->{ interpolator }), it is interesting to see with how many repeated blocks the best performance can be obtained. Cons: 1. The authors claim that the proposed method is an unsupervised learning approach. However, the term “unsupervised” might be misleading. Within the context of frame interpolation, the ground truth second frame is used as a supervision signal for training. Therefore, it is definitely not an “unsupervised” frame interpolation approach. On the other hand, if the “unsupervised learning” refers to estimating optical flow without using the ground truth flow, this paper should compare the learned optical flow with existing unsupervised learning based optical flow methods (such as Ahmadi et al in ICIP2016; Lai et al in NIPS2017; UnFlow in AAAI2018) on the MPI Sintel, KITTI, and Middlebury datasets. 2. I do not see any relationship of the proposed network with the claimed contribution “smoothness constraint” since there is no flow field’s total variation loss adopted in loss function. The paper claims that “The flow network works as a refinement process that blends the smoothness constraint into the raw initial optical flow of pixels through a convolution–deconvolution neural network” (Page5, Line4-6), which has no support from the experimental results to show the difference between before and after refinement. 3. This paper provides a new dataset for video frame interpolation. However, there are only 6 sequences in this dataset, which are not sufficient enough to evaluate various scenes. The challenging cases only include 3 “subtitle”, 2 “occlusion”, and 1 “complex motion” (or say sports scene, halo) sequence, which do not cover the cases of “large motion” and “small object”. The term “halo” used in Table 2/3/4/5 is not consistent with the descriptions in the body text (Page6, Line23-24, “The last sequence captures a soccer match where the movement of players is fast and complex and the ball is a small object.”) and thus should be clarified clearly. 4. Although this paper obtains good results on the Middlebury dataset, it is noticed that this dataset is small with only 8 sequence for frame interpolation. There are larger benchmark datasets used in previous papers, such as the UCF101 in Liu et al in ICCV17 (379 sequences), and Vimeo90K in Xue et al in arxiv17 (3783 sequences). 5. The proposed method consists of two SepConv and one Flow network, which result in approximately 3 times more parameters than the SepConv (21M parameters) model. To validate the proposed network architecture, an ablation study might be conducted by reducing the network parameters to a comparable capacity with SepConv, so that the contributions of the proposed network architecture can be understood well. Also note that CtxSyn by Niklaus et al in CVPR18 has about 13M parameters which is the second best method in Table 1. 6. Since the first interpolator network can generate intermediate frames, it is better to evaluate the intermediate results as well. In that way, we can observe the improvements made from the Flow Net and Interpolator Net 2 for a better understanding of the proposed model. 7. The Standard Deviation (SD) interpolation error is not defined in the paper. More importantly, since Middlebury benchmark only evaluates the Interpolation Error (IE) and Normalized Interpolation Error, I think the authors might mistakenly use SD to represent Normalized Interpolation Error (NIE). Please check the definition of NIE in Baker et al.’s paper in IJCV 2011(“A Database and Evaluation Methodology for Optical Flow”). It’s suggested to use the common evaluation metrics for a fair comparison. 8. The notations for flow and interpolator network in Section 3 and the Figure 1 is messy. There are “Flow net”, “Flow Network”, “Flow Net”, “Flow network”, and “Interpolator network”, “Interpolator Net”, “interpolator network”.