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Action Recognition On Diving 48

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
ORViT TimeSformer	88.0	Object-Region Video Transformers
VIMPAC	85.5	VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
SlowFast	77.6	SlowFast Networks for Video Recognition
LVMAE	94.9	Extending Video Masked Autoencoders to 128 frames	-
StructVit-B-4-1	88.3	Learning Correlation Structures for Vision Transformers	-
TimeSformer	75	Is Space-Time Attention All You Need for Video Understanding?
DUALPATH	88.7	Dual-path Adaptation from Image to Video Transformers
TimeSformer-HR	78	Is Space-Time Attention All You Need for Video Understanding?
TFCNet	88.3	TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal Reasoning	-
Video-FocalNet-B	90.8	Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
AIM (CLIP ViT-L/14, 32x224)	90.6	AIM: Adapting Image Models for Efficient Video Action Recognition
RSANet-R50 (16 frames, ImageNet pretrained, a single clip)	84.2	Relational Self-Attention: What's Missing in Attention for Video Understanding
GC-TDN	87.6	Group Contextualization for Video Recognition
BEVT	86.7	BEVT: BERT Pretraining of Video Transformers
PMI Sampler	81.3	PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
TQN	81.8	Temporal Query Networks for Fine-grained Video Understanding	-
TimeSformer-L	81	Is Space-Time Attention All You Need for Video Understanding?
PSB	86	Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition

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