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Found 20 papers, 15 papers with code
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Deep Affinity Net: Instance Segmentation via Affinity
Most of the modern instance segmentation approaches fall into two categories: region-based approaches in which object bounding boxes are detected first and later used in cropping and segmenting instances; and keypoint-based approaches in which individual instances are represented by a set of keypoints followed by a dense pixel clustering around those keypoints.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e. g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models.
UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution
In this work, we propose UltraSR, a simple yet effective new network design based on implicit image functions in which we deeply integrated spatial coordinates and periodic encoding with the implicit neural representation.
Towards Layer-wise Image Vectorization
Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge.
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate.
Space-time Video Super-resolution
Video Frame Interpolation
+1
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.
Ranked #1 on
Image Inpainting
on FFHQ 512 x 512
StyleNAT: Giving Each Head a New Perspective
Image generation has been a long sought-after but challenging task, and performing the generation task in an efficient manner is similarly difficult.
Ranked #2 on
Image Generation
on FFHQ 256 x 256
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
In this work, we expand the existing single-flow diffusion pipeline into a multi-task multimodal network, dubbed Versatile Diffusion (VD), that handles multiple flows of text-to-image, image-to-text, and variations in one unified model.
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.
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose PAIR Diffusion, a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in the image.
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models
The unlearning problem of deep learning models, once primarily an academic concern, has become a prevalent issue in the industry.
Zero-shot Generative Model Adaptation via Image-specific Prompt Learning
Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain.
Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
Text-to-image (T2I) research has grown explosively in the past year, owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches.
Reference-based Painterly Inpainting via Diffusion: Crossing the Wild Reference Domain Gap
We propose Reference-based Painterly Inpainting, a novel task that crosses the wild reference domain gap and implants novel objects into artworks.
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models
Specifically, we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step.
VASE: Object-Centric Appearance and Shape Manipulation of Real Videos
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models.
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models
In this paper, we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set.
