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Found 9 papers, 2 papers with code
Rethinking Learning Approaches for Long-Term Action Anticipation
Action anticipation involves predicting future actions having observed the initial portion of a video.
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks.
Activity Graph Transformer for Temporal Action Localization
Detecting and localizing action instances in untrimmed videos requires reasoning over multiple action instances in a video.
Ranked #3 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.1 metric)
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
We present a mutual information-based framework for unsupervised image-to-image translation.
Generating Videos of Zero-Shot Compositions of Actions and Objects
Human activity videos involve rich, varied interactions between people and objects.
Variational Selective Autoencoder
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
Learning from Partially-Observed Multimodal Data with Variational Autoencoders
Learning from only partially-observed data for imputation has been an active research area.
Continuous Graph Flow
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data.
Lifelong GAN: Continual Learning for Conditional Image Generation
This makes it possible to perform image-conditioned generation tasks in a lifelong learning setting.
