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Found 7 papers, 2 papers with code
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models
The ability to fine-tune generative models for text-to-image generation tasks is crucial, particularly facing the complexity involved in accurately interpreting and visualizing textual inputs.
Learning and Evaluating Human Preferences for Conversational Head Generation
In this paper, we propose a novel learning-based evaluation metric named Preference Score (PS) for fitting human preference according to the quantitative evaluations across different dimensions.
Interactive Conversational Head Generation
Based on ViCo and ViCo-X, we define three novel tasks targeting the interaction modeling during the face-to-face conversation: 1) responsive listening head generation making listeners respond actively to the speaker with non-verbal signals, 2) expressive talking head generation guiding speakers to be aware of listeners' behaviors, and 3) conversational head generation to integrate the talking/listening ability in one interlocutor.
Visual-Aware Text-to-Speech
Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction.
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots.
Augmentation Pathways Network for Visual Recognition
Experimental results on ImageNet demonstrate the compatibility and effectiveness on a much wider range of augmentations, while consuming fewer parameters and lower computational costs at inference time.
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Ranked #17 on
Fine-Grained Image Classification
on CUB-200-2011
