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Found 19 papers, 9 papers with code
Self-supervised Motion Representation via Scattering Local Motion Cues
Motion representation is key to many computer vision problems but has never been well studied in the literature.
Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression
Most video compression methods aim to improve the decoded video visual quality, instead of particularly guaranteeing the semantic-completeness, which deteriorates downstream video analysis tasks, e. g., action recognition.
Enhanced Deep Animation Video Interpolation
With AutoFI and SktFI, the interpolated animation frames show high perceptual quality.
A Coding Framework and Benchmark towards Compressed Video Understanding
However, in real-world scenarios, the videos are first compressed before the transportation and then decompressed for understanding.
Bounds all around: training energy-based models with bidirectional bounds
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train.
Wood-leaf classification of tree point cloud based on intensity and geometrical information
Tree point cloud was classified into wood points and leaf points by using intensity threshold, neighborhood density and voxelization successively.
Self-Conditioned Probabilistic Learning of Video Rescaling
After optimization, the downscaled video by our framework preserves more meaningful information, which is beneficial for both the upscaling step and the downstream tasks, e. g., video action recognition task.
EAN: Event Adaptive Network for Enhanced Action Recognition
In this paper, we propose a unified action recognition framework to investigate the dynamic nature of video content by introducing the following designs.
Ranked #14 on
Action Recognition
on Something-Something V1
Prediction-assistant Frame Super-Resolution for Video Streaming
An effective approach is to transmit frames in lower-quality under poor bandwidth conditions, such as using scalable video coding.
Generative Model without Prior Distribution Matching
Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e. g., Gaussian distribution).
Perceptron Synthesis Network: Rethinking the Action Scale Variances in Videos
More specifically, an \textit{action perceptron synthesizer} is proposed to generate the kernels from a bag of fixed-size kernels that are interacted by dense routing paths.
Content Adaptive and Error Propagation Aware Deep Video Compression
Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets.
Blurry Video Frame Interpolation
Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation.
Uniform Interpolation Constrained Geodesic Learning on Data Manifold
Based on this defined Riemannian metric, we introduce a constant speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolation along the learned geodesic on the manifold.
Depth-Aware Video Frame Interpolation
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #5 on
Video Frame Interpolation
on Middlebury
DVC: An End-to-end Deep Video Compression Framework
Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed.
Ranked #22 on
Video Frame Interpolation
on Vimeo90K
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement
In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation.
Ranked #6 on
Video Frame Interpolation
on Middlebury
Deep Kalman Filtering Network for Video Compression Artifact Reduction
In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network.
