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HyperAI超神经

Grounded Situation Recognition On Swig

评估指标

Top-1 Verb

Top-1 Verb u0026 Value

Top-5 Verbs

Top-5 Verbs u0026 Value

评测结果

各个模型在此基准测试上的表现结果

					Paper Title	Repository
Ours (CoFormer+)	58.88	51.10	-	-	Dynamic Scene Understanding from Vision-Language Representations	-
ClipSitu	58.19	47.23	85.69	68.42	ClipSitu: Effectively Leveraging CLIP for Conditional Predictions in Situation Recognition
CoFormer	44.66	35.98	73.31	57.76	Collaborative Transformers for Grounded Situation Recognition
SituFormer	44.2	35.24	71.21	55.75	Rethinking the Two-Stage Framework for Grounded Situation Recognition
Kernel GraphNet	43.27	35.41	68.72	55.62	Mixture-Kernel Graph Attention Network for Situation Recognition	-
GSRTR	40.63	32.15	69.81	54.13	Grounded Situation Recognition with Transformers
JSL	39.94	31.44	67.6	51.88	Grounded Situation Recognition
ISL	39.36	30.09	65.51	50.16	Grounded Situation Recognition
CAQ + RE-VGG	38.19	30.23	65.05	50.21	Attention-Based Context Aware Reasoning for Situation Recognition	-
GraphNet	36.72	27.52	61.90	45.39	Situation Recognition with Graph Neural Networks
RNN + Fusion	35.9	27.45	63.08	46.88	Recurrent Models for Situation Recognition	-
CRF + Aug	34.12	26.45	62.59	46.88	Commonly Uncommon: Semantic Sparsity in Situation Recognition
CRF	32.34	24.64	58.88	42.76	Situation Recognition: Visual Semantic Role Labeling for Image Understanding	-

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Grounded Situation Recognition On Swig | SOTA | HyperAI超神经