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Models/Kinet

Kinet

Reported on 14 benchmarks across 7 tasks · 1 paper · 14 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

VideoonNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
VideoonNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Temporal Action LocalizationonNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Temporal Action LocalizationonNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Action LocalizationonNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Action LocalizationonNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113

Methodology2 results

Zero-Shot LearningonNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Zero-Shot LearningonNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113

Robots2 results

Activity RecognitiononNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Activity RecognitiononNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113

Natural Language Processing2 results

3D Action RecognitiononNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
3D Action RecognitiononNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113

Time Series2 results

Action RecognitiononNTU RGB+D
Cross Subject Accuracy· 2022-03-21
92.3
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113
Action RecognitiononNTU RGB+D
Cross View Accuracy· 2022-03-21
96.4
best: 96.7 (PSTNet++)
SOTA
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces arXiv:2203.11113