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cs.CV2024

VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models

Shicheng Li, Lei Li, Shuhuai Ren +5

The ability to perceive how objects change over time is a crucial ingredient in human intelligence. However, current benchmarks cannot faithfully reflect the temporal understanding…

cs.CV2024

TempCompass: Do Video LLMs Really Understand Videos?

Yuanxin Liu, Shicheng Li, Yi Liu +6

Recently, there is a surge in interest surrounding video large language models (Video LLMs). However, existing benchmarks fail to provide a comprehensive feedback on the temporal p…

cs.CV2024

DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models

Linli Yao, Lei Li, Shuhuai Ren +4

The visual projector, which bridges the vision and language modalities and facilitates cross-modal alignment, serves as a crucial component in MLLMs. However, measuring the effecti…

cs.CV2024

Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality

Sishuo Chen, Lei Li, Shuhuai Ren +5

Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries. However, the existing…

cs.CV2024

TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding

Shuhuai Ren, Linli Yao, Shicheng Li +2

This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contr…