From the 1 of 7 linked papers with an AI index.
7 papers
TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs
Yuhan Zhu, Changlian Ma, Xiangyu Zeng +12
Video multimodal large language models (MLLMs) can describe what happens in a video, but rarely identify when the supporting evidence occurs. We study generalist video temporal gro…
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24
VideoChat3 is a fully open, 4B-parameter video-centric multimodal large language model that combines an efficient Inflated 3D Vision Transformer and adaptive frame resolution with…
Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
Cheng Cui, Ting Sun, Suyin Liang +15
Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resol…
TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs
Jun Zhang, Teng Wang, Yuying Ge +4
This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in…
PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks
Cheng Cui, Yubo Zhang, Ting Sun +11
The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recognition. However, these unified architectures often come with significant c…
p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay
Jun Zhang, Desen Meng, Zhengming Zhang +3
Despite the remarkable performance of multimodal large language models (MLLMs) across diverse tasks, the substantial training and inference costs impede their advancement. In this…