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Found 13 papers, 2 papers with code
Masked Autoencoder for Unsupervised Video Summarization
Summarizing a video requires a diverse understanding of the video, ranging from recognizing scenes to evaluating how much each frame is essential enough to be selected as a summary.
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection
Temporal action detection aims to predict the time intervals and the classes of action instances in the video.
Exploring Temporally Dynamic Data Augmentation for Video Recognition
The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations.
Frequency Selective Augmentation for Video Representation Learning
FreqAug stochastically removes specific frequency components from the video so that learned representation captures essential features more from the remaining information for various downstream tasks.
Test-Time Adaptation for Out-of-distributed Image Inpainting
From this test-time adaptation, our network can exploit externally learned image priors from the pre-trained features as well as the internal prior of the test image explicitly.
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features
For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features through normalizing flow (NF)-based generative models to learn the tractable likelihoods and identify anomalies using out of distribution detection.
Smoother Network Tuning and Interpolation for Continuous-level Image Processing
Extensive results for various image processing tasks indicate that the performance of FTN is comparable in multiple continuous levels, and is significantly smoother and lighter than that of other frameworks.
Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks
The postprocessing network increases the quality of decoded images using an example-based learning.
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
Based on our novel temporal data augmentation algorithms, video recognition performances are improved using only a limited amount of training data compared to the spatial-only data augmentation algorithms, including the 1st Visual Inductive Priors (VIPriors) for data-efficient action recognition challenge.
Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation
Unlike previous studies that usually have been focused on the design of modules or construction of networks, we propose a novel Extrapolative-Interpolative Cycle (EIC) loss using pre-trained frame interpolation module to improve extrapolation performance.
Regularized Adaptation for Stable and Efficient Continuous-Level Learning on Image Processing Networks
In this paper, we propose a novel continuous-level learning framework using a Filter Transition Network (FTN) which is a non-linear module that easily adapt to new levels, and is regularized to prevent undesirable side-effects.
Relational Deep Feature Learning for Heterogeneous Face Recognition
Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information.
AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation
Video frame interpolation is one of the most challenging tasks in video processing research.
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Video Frame Interpolation
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