Zero Shot Action Recognition On Kinetics

评估指标

Top-1 Accuracy

Top-5 Accuracy

评测结果

各个模型在此基准测试上的表现结果

			Paper Title	Repository
TC-CLIP	78.1	95.7	Leveraging Temporal Contextualization for Video Action Recognition
IMP-MoE-L	76.8	-	Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception	-
OST	75.1	94.6	OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
MAXI	71.6	-	MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
OTI（ViT-L/14）	70.6	-	Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
VideoCoCa	70.1	88.9	VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners	-
Text4Vis	68.9	90.3	Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
BIKE	68.5	91.1	Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models
X-CLIP	65.2	86.1	Expanding Language-Image Pretrained Models for General Video Recognition
LanguageBind	64.1	85.7	LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
LoCATe-GAT	58.7	-	LoCATe-GAT: Modeling Multi-Scale Local Context and Action Relationships for Zero-Shot Action Recognition	-
JigsawNet	45.9	78.8	Rethinking Zero-shot Action Recognition: Learning from Latent Atomic Actions	-
ER-ZSAR (ST+Obj)	42.1	73.1	Elaborative Rehearsal for Zero-shot Action Recognition
ER-ZSAR (ST)	37.1	69.3	Elaborative Rehearsal for Zero-shot Action Recognition
DEVISE	23.8	51.0	DeViSE: A Deep Visual-Semantic Embedding Model	-
DEM	23.6	49.5	Learning a Deep Embedding Model for Zero-Shot Learning
ALE	23.4	50.3	Label-Embedding for Image Classification
ESZSL	22.9	48.3	An embarrassingly simple approach to zero-shot learning	-
GCN	22.3	49.7	All About Knowledge Graphs for Actions	-
SJE(Word Embedding)	22.3	48.2	Evaluation of Output Embeddings for Fine-Grained Image Classification

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