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- Domain Generalization via Entropy Regularization327
This work proposes an entropy regularization term that measures the dependency between the learned features and the class labels that is guaranteed to learn conditional-invariant features across all source domains and thus can learn classifiers with better generalization capabilities.
- Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation191
This work proposes a geometry-aware symmetric domain adaptation framework (GASDA) to explore the labels in the synthetic data and epipolar geometry in the real data jointly and achieves better image style transfer and generates high-quality depth maps.
- Recent Advances for Quantum Neural Networks in Generative Learning143
This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum variational autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relations and their fundamental differences.
- Short-Term and Long-Term Context Aggregation Network for Video Inpainting58
This work presents a novel context aggregation network to effectively exploit both short-term and long-term frame information for video inpainting, and outperforms state-of-the-art methods with better in painting results and fast inPainting speed.
- AIM 2020: Scene Relighting and Illumination Estimation Challenge50
The novel VIDIT dataset used in the AIM 2020 challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks are presented.
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- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
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- UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results38
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
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- AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results24
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020 and gauges the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM.
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- Single image motion deblurring with reduced ringing effects using variational Bayesian estimation15
Experimental results demonstrate that the blind restoration algorithm that is proposed in this paper can effectively remove image blurring caused by camera shake and obtain restored images that were clearer and more reliable.
- Weakly Supervised Video Moment Retrieval via Location-irrelevant Proposal Learning13
The proposed Location-irrelevant Proposal Learning (LPL) model is based on a pretrained video-text model with cross-modal prompt learning, together with different strategies to generate reasonable proposals with various lengths, which benefits from training with fine-grained video-text pairs.
- Deep Corner11
An end-to-end deep network, named Deep Corner, is developed, which adds a local similarity-based keypoint measure into a plain convolutional network and proposes a feature self-transformation operation, which transforms the learned features adaptively according to the specific local information.
- Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning11
The proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures and is implemented in a unified end-to-end deep learning framework.
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- PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions9
A novel Pixel-wise Shift Learning (PSL) module is developed to estimate the transition from clean to noisy labels as a displacement field, and a local edge density regularization term is devised to exploit local structure information for better transition learning.
- Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation6
The experimental results on several datasets show that in the proposed setting, the developed CoMoDaL can achieve segmentation without the supervision of labeled LiDAR data.
- Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation5
This work proposes ScatterNet, a network that includes three pivotal strategies to reduce the performance gap caused by LiDAR semantic segmentation using scatter image annotation, effectively integrating an efficient annotation strategy with network training.
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- Deep feature dendrite with weak mapping for small-sample hyperspectral image classification2
This paper proposes a Deep Feature Dendrite (DFD) method, which consists of two parts: a deep feature extraction part that uses a convolution-tokenization-attention module to effectively extract spatial-spectral features and a controllable mapping part that uses a residual dendrite network to perform weak mapping and enhance generalization ability.
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- Influence of Medical Implants in the Subcranial and Facial Area on the Accuracy of Quantitative Magnetic Resonance Relaxometry at 3T–
To assess how craniofacial metal implants affect T1 and T2 relaxation time quantification using MRF at 3T, standardized MRI phantoms with cranial implants using MRF and traditional T1/T2 mapping sequences were conducted.
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- Application of improved Multiscale normalization–
A method is applicated, NJW algorithm is replaced by a improved Graph cut which combines the multi-scale analysis method, to improve the efficiency of the original segmentation method and enhance the clinical usability.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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