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Found 14 papers, 6 papers with code
AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition
With the advancement of generative models, the synthesis of different sensory elements such as music, visuals, and speech has achieved significant realism.
Responsible Visual Editing
To mitigate the negative implications of harmful images on research, we create a transparent and public dataset, AltBear, which expresses harmful information using teddy bears instead of humans.
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models.
ORES: Open-vocabulary Responsible Visual Synthesis
In this work, we formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts while allowing users to input any desired content.
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation.
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
3D photography renders a static image into a video with appealing 3D visual effects.
Ranked #1 on
Image Outpainting
on MSCOCO
NUWA-LIP: Language-Guided Image Inpainting With Defect-Free VQGAN
Language-guided image inpainting aims to fill the defective regions of an image under the guidance of text while keeping the non-defective regions unchanged.
ImaginaryNet: Learning Object Detectors without Real Images and Annotations
Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model deployed to generate a photo-realistic image.
NÜWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN
Language guided image inpainting aims to fill in the defective regions of an image under the guidance of text while keeping non-defective regions unchanged.
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification
The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks.
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately.
CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP
Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training.
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain.
