HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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HyperAI초신경

Action Recognition On Diving 48

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Accuracy
Paper TitleRepository
ORViT TimeSformer88.0Object-Region Video Transformers
VIMPAC85.5VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
SlowFast77.6SlowFast Networks for Video Recognition
LVMAE94.9Extending Video Masked Autoencoders to 128 frames-
StructVit-B-4-188.3Learning Correlation Structures for Vision Transformers-
TimeSformer75Is Space-Time Attention All You Need for Video Understanding?
DUALPATH88.7Dual-path Adaptation from Image to Video Transformers
TimeSformer-HR78Is Space-Time Attention All You Need for Video Understanding?
TFCNet88.3TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal Reasoning-
Video-FocalNet-B90.8Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
AIM (CLIP ViT-L/14, 32x224)90.6AIM: Adapting Image Models for Efficient Video Action Recognition
RSANet-R50 (16 frames, ImageNet pretrained, a single clip)84.2Relational Self-Attention: What's Missing in Attention for Video Understanding
GC-TDN87.6Group Contextualization for Video Recognition
BEVT86.7BEVT: BERT Pretraining of Video Transformers
PMI Sampler81.3PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
TQN81.8Temporal Query Networks for Fine-grained Video Understanding-
TimeSformer-L81Is Space-Time Attention All You Need for Video Understanding?
PSB86Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition
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