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Found 8 papers, 2 papers with code
GANHead: Towards Generative Animatable Neural Head Avatars
To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars.
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.
Multi-Granularity Network with Modal Attention for Dense Affective Understanding
Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation.
Progressive Domain Expansion Network for Single Domain Generalization
A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains.
Not All Words are Equal: Video-specific Information Loss for Video Captioning
To address this issue, we propose a novel learning strategy called Information Loss, which focuses on the relationship between the video-specific visual content and corresponding representative words.
DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding
1) A novel compact representation of video which distills its significant spatial-temporal evolution into a matrix called DenseImage, primed for efficient video encoding.
APE-GAN: Adversarial Perturbation Elimination with GAN
Although neural networks could achieve state-of-the-art performance while recongnizing images, they often suffer a tremendous defeat from adversarial examples--inputs generated by utilizing imperceptible but intentional perturbation to clean samples from the datasets.
Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description
This paper contributes to: 1)The first in-depth study of the weakness inherent in data-driven static fusion methods for video captioning.
