MVBench
9 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in MVBench
Most implemented papers
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
opengvlab/ask-anything
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CVPR 2024
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models.
CogVLM2: Visual Language Models for Image and Video Understanding
thudm/glm-4
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Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications.
ST-LLM: Large Language Models Are Effective Temporal Learners
In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs?
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
magic-research/PLLaVA
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arXiv 2024
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
Building on the advances of language models, Large Multimodal Models (LMMs) have contributed significant improvements in video understanding.
Video-CCAM: Enhancing Video-Language Understanding with Causal Cross-Attention Masks for Short and Long Videos
qq-mm/video-ccam
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To address these limitations, we apply cross-attention layers in the intermediate projector between the visual encoder and the large language model (LLM).
Enhancing Temporal Modeling of Video LLMs via Time Gating
lavi-lab/tg-vid
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However, most existing Video LLMs neglect temporal information in video data, leading to struggles with temporal-aware video understanding.
TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models
For video understanding tasks, training-based video LLMs are difficult to build due to the scarcity of high-quality, curated video-text paired data.
Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition
As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI.
