MSVD-QA

Overview edit

Variants: MSVD-QA

Associated Benchmarks

This dataset is used in 4 benchmarks:

• Zero-Shot Learning - Metrics: Accuracy
• Visual Question Answering (VQA) - Metrics: Accuracy
• Video Question Answering - Metrics: Accuracy
• Visual Question Answering - Metrics: Accuracy

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs (2024) -
• MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding (2024) -
• vid-TLDR: Training Free Token merging for Light-weight Video Transformer (2024) -
• Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models (2023) -
• Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models (2023) -
• COSA: Concatenated Sample Pretrained Vision-Language Foundation Model (2023) -
• VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset (2023) -
• VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending (2023) -
• VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset (2023) -
• MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks (2023) -
• Unmasked Teacher: Towards Training-Efficient Video Foundation Models (2023) -
• MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models (2023) -
• MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling (2023) -
• mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video (2023) -
• HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training (2022) -
• VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners (2022) -
• InternVideo: General Video Foundation Models via Generative and Discriminative Learning (2022) -
• X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks (2022) -
• OmniVL:One Foundation Model for Image-Language and Video-Language Tasks (2022) -
• An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling (2022) -