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Found 7 papers, 5 papers with code
CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility.
Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation
In this paper, we explore this objective and propose LaVi-Bridge, a pipeline that enables the integration of diverse pre-trained language models and generative vision models for text-to-image generation.
ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation
Personalized text-to-image generation using diffusion models has recently emerged and garnered significant interest.
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions.
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better
We empirically demonstrate the effectiveness of our RSLAD approach over existing adversarial training and distillation methods in improving the robustness of small models against state-of-the-art attacks including the AutoAttack.
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
In this paper, we focus on image classification and propose a method to visualize and understand the class-wise knowledge (patterns) learned by DNNs under three different settings including natural, backdoor and adversarial.
Clean-Label Backdoor Attacks on Video Recognition Models
We propose the use of a universal adversarial trigger as the backdoor trigger to attack video recognition models, a situation where backdoor attacks are likely to be challenged by the above 4 strict conditions.
