Search Results for author:
Found 12 papers, 5 papers with code
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment.
UGPNet: Universal Generative Prior for Image Restoration
UGPNet first restores the image structure of a degraded input using a regression model and synthesizes a perceptually-realistic image with a generative model on top of the regressed output.
Expanded Adaptive Scaling Normalization for End to End Image Compression
Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation.
N-RPN: Hard Example Learning for Region Proposal Networks
The region proposal task is to generate a set of candidate regions that contain an object.
Exploring Discontinuity for Video Frame Interpolation
Lastly, we propose loss functions to give supervisions of the discontinuous motion areas which can be applied along with FTM and D-map.
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.
Ranked #3 on
3D Point Cloud Classification
on ModelNet40-C
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.
AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation
Video frame interpolation is one of the most challenging tasks in video processing research.
Ranked #14 on
Video Frame Interpolation
on X4K1000FPS
