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Found 9 papers, 6 papers with code
Video Instance Matting
To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.
Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else
To achieve this goal, we identify the limitations in the text embeddings used for the pre-trained text-to-image diffusion models.
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets.
MI-GAN: A Simple Baseline for Image Inpainting on Mobile Devices
In this paper we present a simple image inpainting baseline, Mobile Inpainting GAN (MI-GAN), which is approximately one order of magnitude computationally cheaper and smaller than existing state-of-the-art inpainting models, and can be efficiently deployed on mobile devices.
Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models To Learn Any Unseen Style
Diffusion models have demonstrated impressive capability of text-conditioned image synthesis, and broader application horizons are emerging by personalizing those pretrained diffusion models toward generating some specialized target object or style.
Mask Matching Transformer for Few-Shot Segmentation
Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results.
Image Completion with Heterogeneously Filtered Spectral Hints
We also prove the effectiveness of our design via ablation studies, from which one may notice that the aforementioned challenges, i. e. pattern unawareness, blurry textures, and structure distortion, can be noticeably resolved.
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Image Inpainting
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VMFormer: End-to-End Video Matting with Transformer
In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.
Image Inpainting with Onion Convolutions
The concept of onion convolution is introduced with the purpose of preserving feature continuities and semantic coherence.
