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SOTA
Aktionensegmentierung
Action Segmentation On Breakfast 1
Action Segmentation On Breakfast 1
Metriken
Acc
Average F1
Edit
F1@10%
F1@25%
F1@50%
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Acc
Average F1
Edit
F1@10%
F1@25%
F1@50%
Paper Title
AdaFocus (newly extracted I3D-features, LT-Context model)
78.0
76.2
78.3
82.1
79.0
67.5
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition
ASQuery
77.9
74.6
78.4
80.7
76.5
66.5
ASQuery: A Query-based Model for Action Segmentation
SF-TMN(ASFormer)
77.0
71.6
77.0
78.7
74.0
62.2
SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition
BaFormer
76.6
72.4
77.3
79.2
74.9
63.2
Efficient Temporal Action Segmentation via Boundary-aware Query Voting
DiffAct
76.4
73.6
78.4
80.3
75.9
64.6
Diffusion Action Segmentation
FACT (efficient hybrid of convolution and transformer model)
76.2
74.7
79.7
81.4
76.5
66.2
FACT: Frame-Action Cross-Attention Temporal Modeling for Efficient Action Segmentation
C2F-TCN
76.0
66.2
69.6
72.2
68.7
57.6
Coarse to Fine Multi-Resolution Temporal Convolutional Network
ASPnet
75.9
70.6
76.3
78.1
72.9
60.8
ASPnet: Action Segmentation With Shared-Private Representation of Multiple Data Sources
BIT
75.5
73.7
79.0
80.6
75.8
64.7
BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation
EUT
75
69.3
74.6
76.2
71.8
59.8
Do we really need temporal convolutions in action segmentation?
CETNet
74.9
71.8
77.8
79.3
74.3
61.9
Cross-Enhancement Transformer for Action Segmentation
LTContext
74.2
70.1
77.0
77.6
72.6
60.1
How Much Temporal Long-Term Context is Needed for Action Segmentation?
ASFormer
73.5
68.0
75.0
76.0
70.6
57.4
ASFormer: Transformer for Action Segmentation
DPRN
71.7
67.9
75.1
75.6
70.5
57.6
Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction
DA
71.0
66.4
73.6
74.2
68.6
56.5
Action Segmentation with Mixed Temporal Domain Adaptation
G2L(SSTDA)
70.8
66.9
74.5
76.3
69.9
54.6
Global2Local: Efficient Structure Search for Video Action Segmentation
RF++-SSTDA
70.8
-
-
-
-
-
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
DS-TCN
70.75
59.6
69.02
67.70
62.05
49.18
Depthwise Separable Temporal Convolutional Network for Action Segmentation
BCN
70.4
63.1
66.2
68.7
65.5
55.0
Boundary-Aware Cascade Networks for Temporal Action Segmentation
SSTDA
70.2
66.4
73.7
75.0
69.1
55.2
Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation
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