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Works87 from public data
- Deep Convolutional Neural Network for Image Deconvolution999
This work develops a deep convolutional neural network to capture the characteristics of degradation, establishing the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts.
- Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching502
A novel cascade CNN architecture composing of two stages that advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details, and shows that residual learning provides more effective refinement.
- 3D Human Pose Estimation in the Wild by Adversarial Learning424
An adversarial learning framework is proposed, which distills the 3D human pose structures learned from the fully annotated dataset to in-the-wild images with only 2D pose annotations and designs a geometric descriptor, which computes the pairwise relative locations and distances between body joints, as a new information source for the discriminator.
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- PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration320
It is indicated that existing IQA methods cannot fairly evaluate GAN-based IR algorithms, and a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) is contributed, which includes the results of GAn-based methods, which are missing in previous datasets.
- Accurate Single Stage Detector Using Recurrent Rolling Convolution297
A novel single stage end-to-end trainable object detection network is proposed by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are deep in context.
- Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning276
This work shows that the coupled EMA teacher causes a performance bottleneck and introduces Dual Student, which replaces the teacher with another student, and defines a novel concept, stable sample, following which a stabilization constraint is designed for the structure to be trainable.
- Spatio-Temporal Filter Adaptive Network for Video Deblurring237
The proposed Spatio-Temporal Filter Adaptive Network (STFAN) takes both blurry and restored images of the previous frame as well as blurry image of the current frame as input, and dynamically generates the spatially adaptive filters for the alignment and deblurring.
- Learning Monocular Depth by Distilling Cross-Domain Stereo Networks221
This paper proposes to use the stereo matching network as a proxy to learn depth from synthetic data and use predicted stereo disparity maps for supervising the monocular depth estimation network.
- Learning Dual Convolutional Neural Networks for Low-Level Vision221
The proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner, and can generate the target signals according to the formation model for each particular application.
- Single View Stereo Matching209
This paper shows for the first time that the monocular depth estimation problem can be reformulated as two sub-problems, a view synthesis procedure followed by stereo matching, with two intriguing properties, namely i) geometrical constraints can be explicitly imposed during inference; ii) demand on labelled depth data can be greatly alleviated.
- Deep Edge-Aware Filters205
This work makes the attempt to learn a big and important family of edge-aware operators from data based on a deep convolutional neural network with a gradient domain training procedure, which gives rise to a powerful tool to approximate various filters without knowing the original models and implementation details.
- Learning Event-Based Motion Deblurring200
This paper starts from a sequential formulation of event-based motion deblurring, then shows how its optimization can be unfolded with a novel end-toend deep architecture, and proposes a differentiable directional event filtering module to effectively extract rich boundary prior from the evolution of events.
- Deep Reasoning with Knowledge Graph for Social Relationship Understanding198
This work has found that the interplay between these two factors can be effectively modeled by a novel structured knowledge graph with proper message propagation and attention and can be efficiently integrated into the deep neural network architecture to promote social relationship understanding.
- Shepard convolutional neural networks190
This paper draws on Shepard interpolation and design Shepard Convolutional Neural Networks (ShCNN) which efficiently realizes end-to-end trainable TVI operators in the network and shows that by adding only a few feature maps in the new Shepard layers, the network is able to achieve stronger results than a much deeper architecture.
- Physics-Based Generative Adversarial Models for Image Restoration and Beyond188
An algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, and image deraining) by generative models with adversarial learning within the GAN framework.
- Learning Event-Driven Video Deblurring and Interpolation163
An effective event-driven video deblurring and interpolation algorithm based on deep convolutional neural networks (CNNs) that achieves superior performance against state-of-the-art methods on both synthetic and real datasets.
- LSTM Pose Machines148
It is shown that if the weight sharing scheme is imposed to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN), which decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos.
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- Look, Listen and Learn — A Multimodal LSTM for Speaker Identification115
A novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input is described which outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.
- DAVANet: Stereo Deblurring With View Aggregation108
This work proposes a novel stereo image deblurring network with Depth Awareness and View Aggregation, named DAVANet, which outperforms state-of-the-art methods in terms of accuracy, speed, and model size.
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- On Vectorization of Deep Convolutional Neural Networks for Vision Tasks89
This paper studied the vectorization process of key building blocks in deep CNNs, in order to better understand and facilitate parallel implementation, and developed and compared six implementations with various degrees of vectorization.
- Learning to See in the Dark with Events85
A novel unsupervised domain adaptation network is proposed that explicitly separates domain-invariant features from the domain-specific ones to ease representation learning and achieves superior performance than various state-of-the-art architectures.
- 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.
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 2026-10-12. 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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