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Learning Rank Constrained Exposure Correction from Unpaired Data
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
This paper presents a novel unpaired learning based exposure correction network that can robustly deal with both under- and over-exposed images. Given an input image in any exposure conditions, we first obtain its intermediate under- and over-exposure corrected versions by predicting dual illuminations for Retinex-based enhancement, the two outputs together with the original input are then fed to ...Show More
Self-Supervised Image Harmonization via Holistic Feature Fusion
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Image harmonization is crucial for image composition, aiming to adjust the appearance of the foreground objects to be visually consistent to the background image, so as to produce a realistic yet natural composite image. Due to the difficulty in collecting large-scale annotated datasets, investigating self-supervised image harmonization methods has become a trend, while existing self-supervised me...Show More
Towards Photorealistic Portrait Style Transfer in Unconstrained Conditions
IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Early Access Article |
We present a photorealistic portrait style transfer approach that allows for producing high-quality results in previously challenging unconstrained conditions, e.g., large facial perspective difference between portraits, faces with complex illumination (e.g., shadow and highlight) and occlusion, and can test without portrait parsing masks. We achieve this by developing a framework to learn robust ...Show More
Portrait Shadow Removal Using Context-Aware Illumination Restoration Network
Jiangjian Yu;Ling Zhang;Qing Zhang;Qifei Zhang;Daiguo Zhou;Chao Liang;Chunxia Xiao
Portrait shadow removal is a challenging task due to the complex surface of the face. Although existing work in this field makes substantial progress, these methods tend to overlook information in the background areas. However, this background information not only contains some important illumination cues but also plays a pivotal role in achieving lighting harmony between the face and the backgrou...Show More
Learning Motion-Guided Multi-Scale Memory Features for Video Shadow Detection
Junhao Lin;Jiaxing Shen;Xin Yang;Huazhu Fu;Qing Zhang;Ping Li;Bin Sheng;Liansheng Wang;Lei Zhu
Natural images often contain multiple shadow regions, and existing video shadow detection methods tend to fail in fully identifying all shadow regions, since they mainly learned temporal features at single-scale and single memory. In this work, we develop a novel convolutional neural network (CNN) to learn motion-guided multi-scale memory features to obtain multi-scale temporal information based o...Show More
Single-Image SVBRDF Estimation Using Auxiliary Renderings as Intermediate Targets
Yongwei Nie;Jiaqi Yu;Chengjiang Long;Qing Zhang;Guiqing Li;Hongmin Cai
IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Recently, single-image SVBRDF capture is formulated as a regression problem, which uses a network to infer four SVBRDF maps from a flash-lit image. However, the accuracy is still not satisfactory since previous approaches usually adopt endto-end inference strategies. To mitigate the challenge, we propose “auxiliary renderings” as the intermediate regression targets, through which we divide the ori...Show More
Building Coarse to Fine Convex Hulls With Auxiliary Vertices for Palette-Based Image Recoloring
Constructing a convex hull for the pixel colors of an image by viewing them as 3D points can extract a set of palette colors for the image, then image recoloring can be achieved by modifying the palette colors. For better recoloring effect, the convex hull should contain more pixels (inclusive) and be more compact. Otherwise, reconstruction error would occur or the extracted palette color would be...Show More
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
This paper aims to remove specular highlights from a single object-level image. Although previous methods have made some progresses, their performance remains somewhat limited, particularly for real images with complex specular highlights. To this end, we propose a three-stage network to address them. Specifically, given an input image, we first decompose it into the albedo, shading, and specular ...Show More
Document Image Shadow Removal Guided by Color-Aware Background
Ling Zhang;Yinghao He;Qing Zhang;Zheng Liu;Xiaolong Zhang;Chunxia Xiao
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
Existing works on document image shadow removal mostly depend on learning and leveraging a constant background (the color of the paper) from the image. However, the constant background is less representative and frequently ignores other background colors, such as the printed colors, resulting in distorted results. In this paper, we present a color-aware background extraction network (CBENet) for e...Show More
S ^3 Net: Self-Supervised Self-Ensembling Network for Semi-Supervised RGB-D Salient Object Detection
Lei Zhu;Xiaoqiang Wang;Ping Li;Xin Yang;Qing Zhang;Weiming Wang;Carola-Bibiane Schönlieb;C. L. Philip Chen
RGB-D salient object detection aims to detect visually distinctive objects or regions from a pair of the RGB image and the depth image. State-of-the-art RGB-D saliency detectors are mainly based on convolutional neural networks but almost suffer from an intrinsic limitation relying on the labeled data, thus degrading detection accuracy in complex cases. In this work, we present a self-supervised s...Show More
Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction
Tiezheng Ma;Yongwei Nie;Chengjiang Long;Qing Zhang;Guiqing Li
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (86)
This paper presents a high-quality human motion pre-diction method that accurately predicts future human poses given observed ones. Our method is based on the observation that a good “initial guess” of the future poses is very helpful in improving the forecasting accuracy. This mo-tivates us to propose a novel two-stage prediction frame-work, including an init-prediction network that just computes...Show More
Unsupervised Intrinsic Image Decomposition Using Internal Self-Similarity Cues
Qing Zhang;Jin Zhou;Lei Zhu;Wei Sun;Chunxia Xiao;Wei-Shi Zheng
Recent learning-based intrinsic image decomposition methods have achieved remarkable progress. However, they usually require massive ground truth intrinsic images for supervised learning, which limits their applicability on real-world images since obtaining ground truth intrinsic decomposition for natural images is very challenging. In this paper, we present an unsupervised framework that is able ...Show More
Palette-based image recoloring provides a simple yet effective way for color adjustment, which allows users to interactively manipulate the color of an image by editing a compact color palette. While remarkable progress has been made by previous methods, they have the common limitations that may produce unfaithful image recoloring results i.e., the obtained result does not respond faithfully to th...Show More
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
Lingwei Dang;Yongwei Nie;Chengjiang Long;Qing Zhang;Guiqing Li
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (152)
Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstr...Show More
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (162)
In this paper, we propose HF2-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger fl...Show More
A Multi-Task Network for Joint Specular Highlight Detection and Removal
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (49)
Specular highlight detection and removal are fundamental and challenging tasks. Although recent methods have achieved promising results on the two tasks by training on synthetic training data in a supervised manner, they are typically solely designed for highlight detection or removal, and their performance usually deteriorates significantly on real-world images. In this paper, we present a novel ...Show More
Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts (e.g., color distortion, loss of details and uneven exposure). We observed that this is because they fail to ensure the perceptual consistency of visual information between the source underexposed image and its enhanced ...Show More
Collision-Free Video Synopsis Incorporating Object Speed and Size Changes
Yongwei Nie;Zhenkai Li;Zhensong Zhang;Qing Zhang;Tiezheng Ma;Hanqiu Sun
This paper presents a new surveillance video synopsis method which performs much better than previous approaches in terms of both compression ratio and artifact. Previously, a surveillance video was usually compressed by shifting the moving objects of that video forward along the time axis, which inevitably yielded serious collision and chronological disorder artifacts between the shifted objects....Show More
Multi-View Video Synopsis via Simultaneous Object-Shifting and View-Switching Optimization
Zhensong Zhang;Yongwei Nie;Hanqiu Sun;Qing Zhang;Qiuxia Lai;Guiqing Li;Mingyu Xiao
We present a method for synopsizing multiple videos captured by a set of surveillance cameras with some overlapped field-of-views. Currently, object-based approaches that directly shift objects along the time axis are already able to compute compact synopsis results for multiple surveillance videos. The challenge is how to present the multiple synopsis results in a more compact and understandable ...Show More
Effective Video Stabilization via Joint Trajectory Smoothing and Frame Warping
Tiezheng Ma;Yongwei Nie;Qing Zhang;Zhensong Zhang;Hanqiu Sun;Guiqing Li
Video stabilization is usually composed of three stages: feature trajectory extraction, trajectory smoothing, and frame warping. Most previous approaches view them as three separate stages. This paper proposes a method combining the last two stages, namely the trajectory smoothing and frame warping stages, into a single optimization framework. The novelty exists in the way of how we combine them: ...Show More
Interactive Contour Extraction via Sketch-Alike Dense-Validation Optimization
Yongwei Nie;Xu Cao;Ping Li;Qing Zhang;Zhensong Zhang;Guiqing Li;Hanqiu Sun
We propose an interactive contour extraction method inspired by a skill often adopted in sketching: an artist usually sketches an object by first drawing lots of short, directional, and redundant strokes, then following these small strokes to draw the final outline of the object. Our method simulates this process. To extract a contour, our method relies on user interaction, which provides us with ...Show More
Deep Multi-Model Fusion for Single-Image Dehazing
Zijun Deng;Lei Zhu;Xiaowei Hu;Chi-Wing Fu;Xuemiao Xu;Qing Zhang;Jing Qin;Pheng-Ann Heng
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (104)
This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated feature...Show More
Underexposed Photo Enhancement Using Deep Illumination Estimation
Ruixing Wang;Qing Zhang;Chi-Wing Fu;Xiaoyong Shen;Wei-Shi Zheng;Jiaya Jia
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (609)
This paper presents a new neural network for enhancing underexposed photos. Instead of directly learning an image-to-image mapping as previous work, we introduce intermediate illumination in our network to associate the input with expected enhancement result, which augments the network's capability to learn complex photographic adjustment from expert-retouched input/output image pairs. Based on th...Show More
Towards High-Quality Intrinsic Images in the Wild
2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019 | Conference Paper |
Cited by: Papers (13)
We address the intrinsic image decomposition problem for separating an image into its intrinsic images, i.e, a reflectance layer and a shading layer. Although this problem has been studied for decades, it remains a significant challenge, particularly for real-world images. In this paper, we present a novel method for estimating high-quality intrinsic images for real-world images. Our method is bui...Show More
Palette-Based Image Recoloring Using Color Decomposition Optimization
Previous works on palette-based color manipulation typically fail to produce visually pleasing results with vivid color and natural appearance. In this paper, we present an approach to edit colors of an image by adjusting a compact color palette. Different from existing methods that fail to preserve inherent color characteristics residing in the source image, we propose a color decomposition optim...Show More
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