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Found 6 papers, 3 papers with code
C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation
For the task of temporal action segmentation, we propose an encoder-decoder-style architecture named C2F-TCN featuring a "coarse-to-fine" ensemble of decoder outputs.
A Generalized & Robust Framework For Timestamp Supervision in Temporal Action Segmentation
In temporal action segmentation, Timestamp supervision requires only a handful of labelled frames per video sequence.
Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation
Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses unique challenges.
Coarse to Fine Multi-Resolution Temporal Convolutional Network
In this work, we propose a novel temporal encoder-decoder to tackle the problem of sequence fragmentation.
Ranked #3 on
Action Segmentation
on Assembly101
Pretrained equivariant features improve unsupervised landmark discovery
Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines.
Uncertainty Quantification and Deep Ensembles
In fact, we show that standard ensembling methods, when used in conjunction with modern techniques such as mixup regularization, can lead to less calibrated models.
