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SOTA
视觉问答 (VQA)
Visual Question Answering On Msrvtt Qa 1
Visual Question Answering On Msrvtt Qa 1
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
Repository
VLAB
0.496
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
-
MaMMUT
0.495
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
mPLUG-2
0.480
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
MuLTI
0.478
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling
-
Flamingo
0.474
Flamingo: a Visual Language Model for Few-Shot Learning
UMT-L (ViT-L/16)
0.471
Unmasked Teacher: Towards Training-Efficient Video Foundation Models
InternVideo
0.471
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
vid-TLDR (UMT-L)
0.470
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
FrozenBiLM+
0.470
Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models
VideoCoCa
0.463
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners
-
HBI
0.462
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
HiTeA
0.459
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training
-
EMCL-Net
0.458
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Co-Tokenization
.457
Video Question Answering with Iterative Video-Text Co-Tokenization
-
X2-VLM (large)
0.455
X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
X2-VLM (base)
0.45
X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
All-in-one-B
0.443
All in One: Exploring Unified Video-Language Pre-training
Clover
0.441
Clover: Towards A Unified Video-Language Alignment and Fusion Model
OmniVL
0.441
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
-
AIO+MIF
0.440
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
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