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Found 9 papers, 3 papers with code
Affective Image Filter: Reflecting Emotions from Text to Images
In this work, we propose Affective Image Filter (AIF), a novel model that is able to understand the visually-abstract emotions from the text and reflect them to visually-concrete images with appropriate colors and textures.
L-CoIns: Language-Based Colorization With Instance Awareness
Language-based colorization produces plausible colors consistent with the language description provided by the user.
STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction
To solve the information loss problem, the proposed model aims to preserve the spatiotemporal information for videos during the feature extraction and the state transitions, respectively.
STAU: A SpatioTemporal-Aware Unit for Video Prediction and Beyond
In this paper, we propose a SpatioTemporal-Aware Unit (STAU) for video prediction and beyond by exploring the significant spatiotemporal correlations in videos.
STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction
In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction.
UniCoRN: A Unified Conditional Image Repainting Network
Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly.
MAU: A Motion-Aware Unit for Video Prediction and Beyond
The attention module aims to learn an attention map based on the correlations between the current spatial state and the historical spatial states.
Ranked #18 on
Video Prediction
on Moving MNIST
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling
Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services.
Secrecy Limits of Energy Harvesting IoT Networks under Channel Imperfections
The in-depth analysis of secrecy-energy trade-off has also been performed and a comparison has been provided for imperfect and perfect channel estimation cases.
