15 citations · 39 across the 8 of their papers we have counts for
5 papers · 1 filter
Temporal Action Detection with Multi-level Supervision
Baifeng Shi, Qi Dai, Judy Hoffman +3
Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to…
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi, Judy Hoffman, Kate Saenko +2
Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to util…
Spatio-Temporal Action Detection with Multi-Object Interaction
Huijuan Xu, Lizhi Yang, Stan Sclaroff +2
Spatio-temporal action detection in videos requires localizing the action both spatially and temporally in the form of an "action tube". Nowadays, most spatio-temporal action detec…
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Zhekun Luo, Devin Guillory, Baifeng Shi +4
Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multipl…
Revisiting Few-shot Activity Detection with Class Similarity Control
Huijuan Xu, Ximeng Sun, Eric Tzeng +3
Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able…