Action Segmentation On Breakfast 1

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이 벤치마크에서 각 모델의 성능 결과

							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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Action Segmentation On Breakfast 1

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