most citedVideo Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs

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cs.CV2025

Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations

Haitong Liu, Kuofeng Gao, Yang Bai +4

Recently, video-based large language models (video-based LLMs) have achieved impressive performance across various video comprehension tasks. However, this rapid advancement raises…

cs.CV20241 cited

Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolution

Jiarui Yang, Tao Dai, Yufei Zhu +3

Diffusion models represent the state-of-the-art in generative modeling. Due to their high training costs, many works leverage pre-trained diffusion models' powerful representations…

cs.CV20241 cited

Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs

Jinmin Li, Kuofeng Gao, Yang Bai +2

The advent of video-based Large Language Models (LLMs) has significantly enhanced video understanding. However, it has also raised some safety concerns regarding data protection, a…

cs.CV2024

Invertible Residual Rescaling Models

Jinmin Li, Tao Dai, Yaohua Zha +6

Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs…

cs.CV2024

Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling

Jinmin Li, Tao Dai, Jingyun Zhang +5

Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional perf…

cs.CV20241 cited

FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs

Jinmin Li, Kuofeng Gao, Yang Bai +3

Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored. To fill this gap, we propose the first adversarial atta…