HyperAI超神経

Zero Shot Action Recognition On Ucf101

評価指標

Top-1 Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
Top-1 Accuracy
Paper TitleRepository
SVE10.9Semantic Embedding Space for Zero-Shot Action Recognition-
ResT58.7Cross-modal Representation Learning for Zero-shot Action Recognition-
SJE(Attribute)12.0Evaluation of Output Embeddings for Fine-Grained Image Classification
MAXI78.2MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
TC-CLIP85.4Leveraging Temporal Contextualization for Video Action Recognition
BIKE86.6Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models
MOV (ViT-B/16)82.6Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models-
O2A30.3Objects2action: Classifying and localizing actions without any video example-
X-CLIP72.0Expanding Language-Image Pretrained Models for General Video Recognition
ZSECOC15.1Zero-Shot Action Recognition With Error-Correcting Output Codes-
Text4Vis85.8Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
VicTR (ViT-B/16)72.4VicTR: Video-conditioned Text Representations for Activity Recognition-
LoCATe-GAT76.0LoCATe-GAT: Modeling Multi-Scale Local Context and Action Relationships for Zero-Shot Action Recognition
ESZSL15.0An embarrassingly simple approach to zero-shot learning
HAA14.9--
VideoCoCa86.6VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners-
AURL58Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification-
CLASTER53.9CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition-
EVA-CLIP-E/14+83.1EVA-CLIP: Improved Training Techniques for CLIP at Scale
TS-GCN34.2I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs
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