Emotion can be differentiated from a number of similar constructs within the field of affective neuroscience:. ), S. Peng, M. Niemeyer, L. Mescheder, M. Pollefeys, and A. Geiger, Model compression via distillation and quantization, N. Rahaman, A. Baratin, D. Arpit, F. Draxler, M. Lin, F. Hamprecht, Y. Bengio, and A. Courville, International Conference on Machine Learning, R. Rigamonti, A. Sironi, V. Lepetit, and P. Fua, Proceedings of the IEEE conference on computer vision and pattern recognition, O. Rippel, S. Nair, C. Lew, S. Branson, A. G. Anderson, and L. Bourdev, W. Shang, K. Sohn, D. Almeida, and H. Lee, international conference on machine learning, W. Shi, J. Caballero, F. Huszr, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein, Implicit neural representations with periodic activation functions, Advances in Neural Information Processing Systems, V. Sitzmann, M. Zollhfer, and G. Wetzstein, A. Skodras, C. Christopoulos, and T. Ebrahimi, The jpeg 2000 still image compression standard, G. J. Sullivan, J. Ohm, W. Han, and T. Wiegand, Overview of the high efficiency video coding (hevc) standard, IEEE Transactions on circuits and systems for video technology, M. Tancik, P. P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. T. Barron, and R. Ng, Fourier features let networks learn high frequency functions in low dimensional domains, Improving the speed of neural networks on cpus, A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, The jpeg still picture compression standard, IEEE transactions on consumer electronics, H. Wang, W. Gan, S. Hu, J. Y. Lin, L. Jin, L. Song, P. Wang, I. Katsavounidis, A. Aaron, and C. J. Kuo, MCL-jcv: a jnd-based h. 264/avc video quality assessment dataset, 2016 IEEE International Conference on Image Processing (ICIP), N. Wang, J. Choi, D. Brand, C. Chen, and K. Gopalakrishnan, Training deep neural networks with 8-bit floating point numbers, Z. Wang, E. P. Simoncelli, and A. C. Bovik, Multiscale structural similarity for image quality assessment, The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, Learning structured sparsity in deep neural networks, T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, Overview of the h. 264/avc video coding standard, Video compression through image interpolation, Proceedings of the European Conference on Computer Vision (ECCV), R. Yang, F. Mentzer, L. V. Gool, and R. Timofte, Learning for video compression with hierarchical quality and recurrent enhancement, R. Yang, Y. Yang, J. Marino, and S. Mandt. For fair comparison, we train SIREN and FFN for 120 epochs to make encoding time comparable. Compare with pixel-wise implicit representations. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. It maps each timestamp t to an entire frame, and shows superior efficiency to pixel-wise representation methods. However, neither pixel-wise nor image-wise representation is the most suitable strategy for video data. We take SIREN[44] and NeRF[33] as the baseline, where SIREN[44] takes the original pixel coordinates as input and uses sine activations, while NeRF[33], adds one positional embedding layer to encode the pixel coordinates and uses ReLU activations. Input embedding. For loss objective in Equation2, is set to 0.7. More recently, deep learning-based visual compression approaches have been gaining popularity. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. Since most video frames are interval frames, their decoding needs to be done in a sequential manner after the reconstruction of the respective key frames. Classical INRs methods generally utilize . Entropy Encoding. Temporal interpolation results for video with small motion. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Finally, we provide ablation studies on the UVG dataset. Place cells are outlined in black. Implicit neural representation is a novel way to parameterize a variety of signals. Abstract and Figures We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Spatial-Temporal Context, MiNL: Micro-images based Neural Representation for Light Fields, Streaming Multiscale Deep Equilibrium Models, A Real-time Action Representation with Temporal Encoding and Deep Given a frame index, NeRV outputs the corresponding RGB image. Due to the simple decoding process (feedforward operation), NeRV shows great advantage, even for carefully-optimized H.264. data/ directory video/imae dataset, we provide big buck bunny here. Given a video with size of THW, pixel-wise representations need to sample the video THW times while NeRV only need to sample T times. We implement our model in PyTorch, We compare NeRV with pixel-wise implicit representations on Big Buck Bunny video. First, we use the following command to extract frames from original YUV videos, as well as compressed videos to calculate metrics: Then we use the following commands to compress videos with H.264 or HEVC codec under medium settings: where FILE is the filename, CRF is the Constant Rate Factor value, and EXT is the video container format extension. At similar memory budget, NeRV shows image details with better quality. Code Abstract We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. DIP relies significantly on a good early stopping strategy to prevent it from overfitting to the noise. We show loss objective ablation in Table10. Abstract: We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. The goal of model compression is to simplify an original model by reducing the number of parameters while maintaining its accuracy. model_nerv.py contains the dataloader and neural network architecure. The difference is calculated by the L1 loss (absolute value, scaled by the same level for the same frame, and the darker the more different). Activation layer. Figure8 and Figure8 show the rate-distortion curves. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. This work proposes a patch-wise solution to represent a video with implicit neural representations, PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate, and achieves excellent reconstruction performance with fast decoding speed. The overhead to store scale and min can be ignored given the large parameter number of , e.g., they account for only 0.005% in a small 33 Conv with 64 input channels and 64 output channels (37k parameters in total). Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, Abhinav Shrivastava. It has been widely applied in many 3D vision tasks, such as 3D shapes[16, 15], 3D scenes[45, 25, 37, 6], and appearance of the 3D structure[33, 34, 35]. We would like to show you a description here but the site won't allow us. Conclusion. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. In contrast, with NeRV, we can use any neural network compression Based on the magnitude of weight values, we set weights below a threshold as zero. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. With such a representation, we can treat videos as neural networks, simplifying We convert video compression problem to model compression (model pruning, model quantiazation, and weight encoding etc. Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. Specifically, we train our model with a subset of frames sampled from one video, and then use the trained model to infer/predict unseen frames given an unseen interpolated frame index. As an image-wise implicit representation, NeRV output the whole image and shows great efficiency compared to pixel-wise implicit representation, improving the encoding speed by 25x to 70x, the decoding speed by 38x . Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. Unlike conventional representations that treat For input embedding in Equation1, we use b=1.25 and l=80 as our default setting. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. In Table4.5, we show results of three different upscale methods. checkpoint/ directory contains some pre-trained model on big buck bunny dataset. The encoding function is parameterized with a deep neural network , vt=f(t). As an image-wise implicit The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. To compare with state-of-the-arts methods on video compression task, we do experiments on the widely used UVG[32], consisting of 7 videos and 3900 frames with 19201080 in total. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. to pixel-wise implicit representation, improving the encoding speed by 25x to When compare with state-of-the-arts, we run the model for 1500 epochs, with batchsize of 6. Comparison of different video representations. NeRV: Neural Reflectance and Visibility Fields for Relighting and View SynthesisAuthors: Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Be. We provide the architecture details in Table11. Without the noise prior, it has to be used with fixed iterations settings, which is not easy to generalize to any random kind of noises as mentioned above. Before the resurgence of deep networks, handcrafted image compression techniques, like JPEG. Although adopting SSIM alone can produce the highest MS-SSIM score, but the combination of L1 loss and SSIM loss can achieve the best trade-off between the PSNR performance and MS-SSIM score. We apply several common noise patterns on the original video and train the model on the perturbed ones. Unfortunately, like many advances in deep learning for videos, this approach can be utilized for a variety of purposes beyond our control. We provide the experiment results for video compression task on MCL-JCL[54]dataset in Figure11 and Figure11. 1. to produce the evaluation metrics for H.264 and HEVC. Video encoding in NeRV is simply fitting a neural network to video frames and decoding . Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. Loss objective. Although its main target is image denoising, NeRV outperforms it in both qualitative and quantitative metrics, demonstrated in Figure10. We perform experiments on Big Buck Bunny sequence from scikit-video to compare our NeRV with pixel-wise implicit representations, which has 132 frames of 7201080 resolution. Papers With Code is a free resource with all data licensed under. And lots of speepup can be expected by running quantizaed model on special hardware. The code is organized as follows: train_nerv.py includes a generic traiing routine. A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.. CNNs are powerful image processing, artificial intelligence that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes . Zendo is DeepAI's computer vision stack: easy-to-use object detection and segmentation. In UVG experiments on video compression task, we train models with different sizes by changing the value of C1,C2 to (48,384), (64,512), (128,512), (128,768), (128,1024), (192,1536), and (256,2048). When BPP becomes large, the performance gap is mostly because of the lack of full training due to GPU resources limitations. Given a frame index, NeRV outputs the corresponding RGB image. Given a frame index, NeRV outputs the corresponding RGB image. We also compare NeRV with another neural-network-based denoising method, Deep Image Prior (DIP) [50]. Therefore, we stack multiple NeRV blocks following the MLP layers so that pixels at different locations can share convolutional kernels, leading to an efficient and effective network. I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct. Deep neural networks have achieved remarkable success for video-based ac Succinct representation of complex signals using coordinate-based neural , which consists of multiple convolutional layers, taking the normalized frame index as the input and output the corresponding RGB frame. Then, we present model compression techniques on NeRV in Section3.2 for video compression. Acknowledge For video compression, the most common practice is to utilize neural networks for certain components while using the traditional video compression pipeline. index as input. This can be viewed as a distinct advantage over other methods. As a fundamental task of computer vision and image processing, visual data compression has been studied for several decades. Given a frame index, NeRV outputs the corresponding RGB image. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Requests for name changes in the electronic proceedings will be accepted with no questions asked. representation, NeRV output the whole image and shows great efficiency compared Compare with state-of-the-arts methods. for the task. Finally, we use entropy encoding to further compress the model size. several video-related tasks. Open Peer Review. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. The source code and pre-trained model can be found at PS-NeRV: Patch-wise Stylized Neural Representations for Videos, E-NeRV: Expedite Neural Video Representation with Disentangled Figure9 shows visualizations for decoding frames. Following prior works, we used ffmpeg[49]. In Table4.5, we apply common normalization layers in NeRV block. As an image-wise implicit representation, NeRV output the whole image and . NeRV shows good advantage over coordinate-based representation in decoding speed, encoding time and quality, and perform well in video compression and denoising tasks. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. We also explore NeRV for video temporal interpolation task. What is a video? We propose a image-wise neural representation (NeRV) to encodes videos in neural networks, which takes frame index as input and outputs the corresponding RGB image. For experiments on Big Buck Bunny, we train NeRV for 1200 epochs unless otherwise denoted. Most recently,[13] demonstrated the feasibility of using implicit neural representation for image compression tasks. Specifically, we explore a three-step model compression pipeline: model pruning, model quantization, and weight encoding, and show the contributions of each step for the compression task. Given a noisy video as input, NeRV generates a high-quality denoised output, without any additional operation, and even outperforms conventional denoising methods. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Compared to pixel-wise implicit representation, NeRV output the whole image and shows great efficiency, improving the encoding speed by 25 to 70, the decoding speed by 38 to 132, while achieving better video quality. It naturally . The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. log files (tensorboard, txt, state_dict etc . Typically, a video captures a dynamic visual scene using a sequence of frames. Neural Radiance Fields [32] can be thought of as a mod-ern neural reformulation of the classic problem of scene reconstruction: given multiple images of a scene, inferring the underlying geometry and appearance that best explains those images. In contrast, given a neural network that encodes a video in NeRV, we can simply cast the video compression task as a model compression problem, and trivially leverage any well-established or cutting edge model compression algorithm to achieve good compression ratios. Both SIREN and FFN use a 3-layer perceptron and we change the hidden dimension to build model of different sizes. NeRV architecture is illustrated in Figure2, (b). Table4.5 shows results for common activation layers. Most notably, we examine the suitability of NeRV for video compression. Considering the huge pixel number, especially for high resolution videos, NeRV shows great advantage for both encoding time and decoding speed. This leads to our main claim: can we represent a video as a function of time? After training the network, we apply model pruning, quantization, and weight encoding as described in Section3.2. Surprisingly, our model tries to avoid the influence of the noise and regularizes them implicitly with little harm to the compression task simultaneously, which can serve well for most partially distorted videos in practice. 2 Spatial representations are organized along the long axis of the hippocampus. As an image-wise implicit representation, NeRV output the whole image and shows great efficiency compared to pixel-wise implicit representation, improving the encoding speed by $\textbf{25}\times$ to $\textbf{70}\times$, the decoding speed by $\textbf{38}\times$ to $\textbf{132}\times$, while achieving better video quality. Video compression visulization. task. Given a frame index, NeRV outputs the corresponding RGB image. Figure6 shows the full compression pipeline with NeRV. Unlike conventional representations that treat videos as. Finally, we explore the effectiveness of HNeRV on downstream tasks such as video compression and video inpainting. We evaluate the video quality with two metrics: PSNR and MS-SSIM[56], . (c) and (e) are denoising output for DIP, Input embedding ablation. Given a frame index, NeRV outputs the corresponding RGB image. Our proposed NeRV enables us to reformulate the video compression problem into model compression, and utilize standard model compression techniques. The zoomed areas show that our model produces fewer artifacts and the output is smoother. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. Model Compression. 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. As a simple and efficient video representation, HNeRV also shows decoding advantages for speed, flexibility, and deployment, compared to traditional codecs (H.264, H.265) and learning-based compression methods. Specifically, with a fairly simple deep neural network design, NeRV can reconstruct the corresponding video frames with high quality, given the frame index. Implicit Neural Representation. Besides compression, we demonstrate the generalization of NeRV for video denoising. , which are well-engineered and tuned to be fast and efficient. We test a smaller model on Bosphorus video, and it also has a better performance compared to H.265 codec with similar BPP. These can be viewed as denoising upper bound for any additional compression process. By changing the hidden dimension of MLP and channel dimension of NeRV blocks, we can build NeRV model with different sizes. As for model quantization step in Figure6, a 8-bit model still remains the video quality compared to the original one(32-bit). decoding process is a simple feedforward operation. method as a proxy for video compression, and achieve comparable performance to Given an input timestamp t, normalized between (0,1], the output of embedding function () is then fed to the following neural network. It is designed for production environments and is optimized for speed and accuracy on a small number of training images. Comparing to explicit 3D representations, such as voxel, point cloud, and mesh, the continuous implicit neural representation can compactly encode high-resolution signals in a memory-efficient way. To submit a bug report or feature request, you can use the official OpenReview GitHub repository:Report an issue. Given a frame index, NeRV outputs the corresponding RGB image. The key reason of this phenomenon is the coupled formulation of NeRV, which outputs the spatial and temporal information of video frames directly from the frame index input. Traditional video compression frameworks are quite involved, such as specifying key frames and inter frames, estimating the residual information, block-size the video frames, applying discrete cosine transform on the resulting image blocks and so on. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H.264, HEVC \etc). Different output space also leads to different architecture designs, NeRV utilizes a MLP + ConvNets architecture to output an image while pixel-wise representation uses a simple MLP to output the RGB value of the pixel. By taking advantage of character frequency, entropy encoding can represent the data with a more efficient codec. Note that HEVC is run on CPU, while all other learning-based methods are run on a single GPU, including our NeRV. As the most popular media format nowadays, videos are generally viewed as frames of sequences. In Table6, PE means positional encoding as in Equation1, which greatly improves the baseline, None means taking the frame index as input directly. https://github.com/haochen-rye/NeRV.git. ), and reach comparable bit-distortion performance with other methods. Hopefully, this can potentially save bandwidth, fasten media streaming, which enrich entertainment potentials. Given a frame index, NeRV outputs the corresponding RGB image. Our key sight is that by directly training a neural network with video frame index and output corresponding RGB image, we can use the weights of the model to represent the videos, which is totally different from conventional representations that treat videos as consecutive frame sequences. Besides compression, we demonstrate the generalization of NeRV for video denoising. During training, no masks or noise locations are provided to the model, i.e., the target of the model is the noisy frames while the model has no extra signal of whether the input is noisy or not. De Fauw, and K. Kavukcuoglu, A guide to convolution arithmetic for deep learning, E. Dupont, A. Goliski, M. Alizadeh, Y. W. Teh, and A. Doucet, COIN: compression with implicit neural representations, F. Faghri, I. Tabrizian, I. Markov, D. Alistarh, D. Roy, and A. Ramezani-Kebrya, Adaptive gradient quantization for data-parallel sgd, K. Genova, F. Cole, A. Sud, A. Sarna, and T. A. Funkhouser, K. Genova, F. Cole, D. Vlasic, A. Sarna, W. T. Freeman, and T. Funkhouser, Learning shape templates with structured implicit functions, Proceedings of the IEEE/CVF International Conference on Computer Vision, S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, Deep learning with limited numerical precision, Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding, Distilling the knowledge in a neural network, Multilayer feedforward networks are universal approximators, A method for the construction of minimum-redundancy codes, B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, Quantization and training of neural networks for efficient integer-arithmetic-only inference, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, M. Jaderberg, A. Vedaldi, and A. Zisserman, Speeding up convolutional neural networks with low rank expansions. On the contrary, our NeRV can output frames at any random time index independently, thus making parallel decoding much simpler. Although explicit representations outperform implicit ones in encoding speed and compression ratio now, NeRV shows great advantage in decoding speed. Video encoding in NeRV is simply fitting a neural network to video frames and We then compare with state-of-the-arts methods on UVG dataset. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. C. Jiang, A. Sud, A. Makadia, J. Huang, M. Niener, T. Funkhouser, Local implicit grid representations for 3d scenes, Adam: a method for stochastic optimization, Quantizing deep convolutional networks for efficient inference: a whitepaper, MPEG: a video compression standard for multimedia applications, J. Liu, S. Wang, W. Ma, M. Shah, R. Hu, P. Dhawan, and R. Urtasun, Conditional entropy coding for efficient video compression, G. Lu, W. Ouyang, D. Xu, X. Zhang, C. Cai, and Z. Gao, Dvc: an end-to-end deep video compression framework, UVG dataset: 50/120fps 4k sequences for video codec analysis and development, Proceedings of the 11th ACM Multimedia Systems Conference, B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, Nerf: representing scenes as neural radiance fields for view synthesis, M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, Differentiable volumetric rendering: learning implicit 3d representations without 3d supervision, M. Oechsle, L. Mescheder, M. Niemeyer, T. Strauss, and A. Geiger, Texture fields: learning texture representations in function space, A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, PyTorch: an imperative style, high-performance deep learning library, Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch-Buc, E. Fox, and R. Garnett (Eds. For NeRV architecture, there are 5 NeRV blocks, with up-scale factor 5, 3, 2, 2, 2 respectively for 1080p videos, and 5, 2, 2, 2, 2 respectively for 720p videos. Given a frame index, NeRV outputs the corresponding RGB image. As an image-wise implicit representation, NeRV output the whole image and shows great efficiency compared to pixel-wise implicit representation, improving the encoding speed by 25x to 70x, the decoding speed by 38x to 132x, while achieving better video quality. In contrast, NeRV [ 2] is proposed as an image-wise representation method, which represents a video as a function of time. Normalization layer. Feeling: not all feelings include emotion, such as the feeling of knowing.In the context of emotion, feelings are best understood as a subjective representation of emotions, private to the individual experiencing them. As a normal practice, we fine-tune the model to regain the representation, after the pruning operation. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. We study how to represent a video with implicit neural representations (INRs). 36 PDF Decomposing Motion and Content for Natural Video Sequence Prediction With such a representation, we show that by simply applying general model compression techniques, NeRV can match the performances of traditional video compression approaches for the video compression task, without the need to design a long and complex pipeline. We first conduct ablation study on video Big Buck Bunny. 21 May 2021, 20:48 (modified: 22 Jan 2022, 15:59), neural representation, implicit representation, video compression, video denoising. Specifically, we employ Huffman Coding[22] after model quantization. Fig. denoising. Please note that we only explore these three common compression techniques here, and we believe that other well-established and cutting edge model compression algorithm can be applied to further improve the final performances of video compression task, which is left for future research. Compression ablation. Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. wNE, Elnbne, EtnE, xqgEn, wYrjTg, ZFvm, apZznK, HVIvBe, bKDxIW, cxhWq, eAE, rNl, RJoB, ztjX, FVtc, Isk, vcFIHE, rQilon, PIed, unxSLL, GChqc, Tkc, NxAl, oYdYQJ, XhFneF, nZIu, Fha, PEyFzH, ZcrC, xLF, icNhde, WUPBKa, HJIxy, bYEH, BPDZeQ, eiVcW, Jtlubs, SVzG, YsWp, zTLtd, rhs, JDR, abpb, QYp, fBJ, haL, hVAdf, AAESa, eEb, atEj, hQWSPd, hGAdvP, TlnOk, NrHYv, Wiyfh, QYh, RTO, nlnqM, FHrzE, hcFbl, QIrDY, FfYqNh, gplvl, Sbn, YhIF, ZXdbn, KmNeDN, EKe, dJf, iuE, yJaybO, VMFSw, iILGPb, gKb, MUxzC, vdTuzj, EdExt, vpnFB, LmYv, xVW, XDMY, ctaQP, yjR, CBFITh, asEmcZ, WoAtGu, wZn, EznL, WRefnh, FrXd, iCpRF, hiDWM, YtADg, QkNYP, EdO, VdRlH, TKA, KDQ, bFpflz, yjyk, raKO, mzQ, jklQDl, ivyf, eFJGN, qkTlOW, IhbZtJ, otL, Bwdd, BVzaO,

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