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Works39 from public data
- GAN Inversion: A Survey653
This paper provides a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation, and discusses the trends and challenges for future research.
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- Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network137
An Attention-based Hybrid Image Quality Assessment Network (AHIQ) is proposed to deal with the challenge and get better performance on the GAN-based IQA task and outperforms the state-of-the-art methods on four standard IQA datasets.
- NTIRE 2022 Challenge on Perceptual Image Quality Assessment84
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022 to address the emerging challenge of IQA by perceptual image processing algorithms.
- DREAM: Visual Decoding from REversing HumAn Visual SysteM80
DREAM, an fMRI-to-image method for reconstructing viewed images from brain activities, grounded on fundamental knowledge of the human visual system, outperforms the current state-of-the-art models in terms of the consistency of appearance, structure, and semantics.
- NTIRE 2021 Challenge on Perceptual Image Quality Assessment55
The NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021, achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
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- Region-Adaptive Deformable Network for Image Quality Assessment36
The referenceoriented deformable convolution is proposed, which can improve the performance of an IQA network on GAN-based distortion by adaptively considering this misalignment and a patch-level attention module to enhance the interaction among different patch regions, which are processed independently in previous patch-based methods.
- A Survey on Deep Generative 3D-aware Image Synthesis32
This survey aims to introduce new researchers to the task of deep generative 3D-aware image synthesis, provide a useful reference for related works, and stimulate future research directions through the discussion section.
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- Cali-sketch: Stroke calibration and completion for high-quality face image generation from human-like sketches22
Cali-Sketch is proposed, a human-like-sketch to photo-realistic-image generation method that decouple a difficult cross-domain translation problem into two easier steps and is compared with state-of-the-art methods.
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- Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement17
This paper proposes a unified framework for learning to generate diverse outputs using unpaired training data and allow for simultaneous multi-domain translation via a single model and investigates how to extract domain-level signal as explicit supervision so as to achieve better image-to-image translation.
- Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches15
Cali-Sketch is proposed, a poorly-drawn-sketch to photo-realistic-image generation method that decouple a difficult cross-domain translation problem into two easier steps.
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- Exploring the Visual Feature Space for Multimodal Neural Decoding8
This work analyzes different choices of vision feature spaces from pre-trained visual components within Multimodal Large Language Models (MLLMs) and introduces a zero-shot multimodal brain decoding method that interacts with these models to decode across multiple levels of granularities.
- PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding7
A novel framework PoseDet (Estimating Pose by Detection) to localize and associate body joints simultaneously at higher inference speed is presented and the keypoint-aware pose embedding to represent an object in terms of the locations of its keypoints is proposed.
- Real-time human-centric segmentation for complex video scenes6
A novel framework, abbreviated as HVISNet, that segments and tracks all presented people in given videos based on a one-stage detector to improve the accuracy of instance segmentation is proposed, which outperforms the state-of-the-art methods in terms of accuracy at a real-time inference speed.
- Physically Based Neural Bidirectional Reflectance Distribution Function5
This model accurately reconstructs real-world materials while uniquely enforcing physical properties for realistic BRDFs, specifically Helmholtz reciprocity via reparametrization and energy passivity via efficient analytical integration.
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- Identity-Guided Face Generation with Multi-Modal Contour Conditions4
This work proposes a novel dual-encoder architecture, in which an identity encoder extracts the identity-related feature, accompanied by a main encoder to obtain the rough contour information and further fuse all the information together, which achieves identity-guided face generation conditioned on multi-modal contour images.
- A Survey on Text-Driven 360° Panorama Generation3
This survey presents a comprehensive review of text-driven 360-degree panorama generation, offering an in-depth analysis of state-of-the-art algorithms and critically examine current limitations and propose promising directions for future research.
- Modelling Latent Dynamics of StyleGAN using Neural ODEs3
The entire sequence is seen as discrete-time observations of a continuous trajectory of the initial latent code, by considering each latent code as a moving particle and the latent space as a high-dimensional dynamic system.
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- Single-Stage fMRI-to-3D Reconstruction via Viewpoint-Aware Embedding and Hierarchical Guidance1
NeuroSculptor3D is introduced, the first single-stage, end-to-end framework for reconstructing textured 3D shapes directly from brain activity and outperforms existing baselines across multiple settings, achieving significant improvements in both structural accuracy and semantic consistency.
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- Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance1
To the best knowledge, this is the first 3D point cloud generation framework that fully integrates and enforces both symmetry and part priors throughout the generative process.
- RETRO: REthinking Tactile Representation Learning with Material PriOrs1
This work revisiting the tactile representation learning framework and incorporating material-aware priors into the learning process enables more accurate, contextually rich tactile feedback across diverse materials and textures, improving performance in real-world applications such as robotics, haptic feedback systems, and material editing.
- ECGAN: Image Translation with Multi-scale Relativistic Average Discriminator1
An Enhanced CycleGAN (ECGAN) framework with multi-scale relativistic average discriminator, which integrates the loss function design and network structure optimization to make the generated images have more natural textures and fewer unwanted artifacts is proposed.
- Matching Low-Quality Photo to DSLR-Quality with Deep Convolutional Networks1
This work proposes an end-to-end learning approach that translates low-quality photos captured by cameras with limited capabilities into DSLR-quailty photos and improves the existing state-of-art in terms of structural similarity measure, peak signal- to-noise ratio and by visual appearance.
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- PoseCraft: Tokenized 3D Body Landmark and Camera Conditioning for Photorealistic Human Image Synthesis–
PoseCraft is presented, a diffusion framework built around tokenized 3D interface: instead of relying only on rasterized geometry as 2D control images, it encode sparse 3D landmarks and camera extrinsics as discrete conditioning tokens and inject them into diffusion via cross-attention.
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- SR-Net: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions.–
Extensive experiments demonstrate that the proposed SR-Net approach can not only improve the accuracy of high-level vision tasks with image adaption, but also boosts the perceptual quality and structural similarity of degraded images with image semantic guidance.
- Segment for Restoration, Restore for Segmentation–
Extensive experiments demonstrate that the proposed SR-Restore approach can not only improve the accuracy of high-level vision tasks with image adaption, but also boosts the perceptual quality and structural similarity of degraded images with image semantic guidance.
- Domain-Aware No-Reference Image Quality Assessment.–
The domain-aware no-reference image quality assessment (DA-NR-IQA) is proposed, which for the first time exploits and disentangles the distinct representation of different degradations to access image quality.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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