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- 2,959
- 951
- Towards Real-World Blind Face Restoration with Generative Facial Prior676
This work proposes GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration that achieves superior performance to prior art on both synthetic and real-world datasets.
- Effective Whole-body Pose Estimation with Two-stages Distillation446
This work presents a two-stage pose Distillation for Whole-body Pose estimators, named DWPose, to improve their effectiveness and efficiency and releases a series of models with different sizes, from tiny to large, for satisfying various downstream tasks.
- 373
- Instances as Queries362
For the first time, it is demonstrated that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks.
- Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset342
This paper proposes a novel end-to-end attention-guided method based on multi-branch convolutional neural network that can produce high fidelity enhancement results for low-light images and outperforms the current state-of-the-art methods both quantitatively and visually.
- Human Pacman: a mobile, wide-area entertainment system based on physical, social, and ubiquitous computing335
Human Pacman research is a physical role-playing augmented-reality computer fantasy together with real human–social and mobile gaming that emphasizes collaboration and competition between players in a wide outdoor physical area which allows natural wide-area human–physical movements.
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- One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer186
This work designs a one-stage pipeline for expressive whole-body mesh recovery, named OSX, without separate networks for each part, and designs a Component Aware Transformer (CAT) composed of a global body encoder and a local face/hand decoder.
- Seam-Driven Image Stitching182
This work proposes a seam-driven image stitching strategy where instead of estimating a geometric transform based on the best fit of feature correspondences, the goodness of a transform is evaluated based upon the resulting visual quality of the seam-cut.
- 170
- Haze visibility enhancement: A Survey and quantitative benchmarking170
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes and provides a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal and a newly generated scattering media dataset that contains ground truth images for quantitative evaluation.
- From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels166
This work unifies the formulations of the two tasks by decomposing and reorganizing the generic KD loss into a Normalized KD (NKD) loss and customized soft labels for both target class (image’s category) and non-target classes named Universal Self-KD (USKD).
- A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model153
- A Contrast Enhancement Framework with JPEG Artifacts Suppression137
This work proposes a framework that suppresses compression artifacts as an integral part of the contrast enhancement procedure and shows that this approach can produce compelling results superior to those obtained by existing JPEG artifacts removal methods for several types of contrast enhancement problems.
- Towards Vivid and Diverse Image Colorization with Generative Color Prior135
This work aims at recovering vivid colors by leveraging the rich and diverse color priors encapsulated in a pretrained Generative Adversarial Networks (GAN) via a GAN encoder and incorporating these features into the colorization process with feature modulations.
- Unsupervised Learning for Intrinsic Image Decomposition From a Single Image134
This paper proposes a novel unsupervised intrinsic image decomposition framework, which relies on neither labeled training data nor hand-crafted priors, and directly learns the latent feature of reflectance and shading from unsuper supervised and uncorrelated data.
- Fast Video Object Segmentation Using the Global Context Module131
A real-time, high-quality semi-supervised video object segmentation algorithm that effectively summarizes and propagates information through the entire video, and uses constant memory regardless of the video length and costs substantially less memory and computation.
- Crossover Learning for Fast Online Video Instance Segmentation129
A novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames to enable efficient cross-frame instance-to-pixel relation learning and brings cost-free improvement during inference.
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- Fast Guided Global Interpolation for Depth and Motion102
This work proposes a unified approach that casts the fundamental guided interpolation problem into a hierarchical, global optimization framework, and achieves quantitatively competitive results on various benchmark evaluations, while running much faster than other competing methods designed specifically for either depth upsampling or motion interpolation.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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