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Found 8 papers, 6 papers with code
Jailbreaking the Text-to-Video Generative Models
Our approach formulates the prompt generation task as an optimization problem with three key objectives: (1) maximizing the semantic similarity between the input and generated prompts, (2) ensuring that the generated prompts can evade the safety filter of the text-to-video model, and (3) maximizing the semantic similarity between the generated videos and the original input prompts.
T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models
The rapid development of generative artificial intelligence has made text to video models essential for building future multimodal world simulators.
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models.
A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users.
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities.
Ranked #10 on
Zero-Shot Video Retrieval
on LSMDC
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge.
Egocentric Video-Language Pretraining @ Ego4D Challenge 2022
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).
Egocentric Video-Language Pretraining
Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
