action class
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing (Supplementary Material)
In this section, we provide the detailed description of the datasets we used to perform all the experiments for CoMix, namely, (1) UCF-HMDB [3], (2) Jester [13], and (3) Epic-Kitchens [12].
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Specifically, we pose and answer the following questions: Q1. How do the learned spatial and temporal representations vary based on different VSSL pretrain-ing methodologies? How robust are these representations to different distribution shifts?
A flexible model for training action localization with varying levels of supervision
Guilhem Chรฉron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid
Neural Information Processing Systems http://nips.cc/
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction
Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim
Neural Information Processing Systems http://nips.cc/
Action-guided3DHumanMotionPrediction
However, it is still very challenging for machines to achievereasonable forecasting.
TemporalContrastiveVideo SupplementaryMaterial
In this section, we provide the detailed description of the datasets we used to perform all the experimentsforCoMix,namely,(1)UCF-HMDB[3],(2)Jester[13],and(3)Epic-Kitchens[12].
