Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Suggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works88 from public data
- Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection1,828
The proposed memory-augmented autoencoder called MemAE is free of assumptions on the data type and thus general to be applied to different tasks and proves the excellent generalization and high effectiveness of the proposed MemAE.
- From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur439
This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow.
- Attention-Guided Network for Ghost-Free High Dynamic Range Imaging313
The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error, and can achieve state-of-the-art quantitative and qualitative results.
- Knowledge Adaptation for Efficient Semantic Segmentation253
This work proposes a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride and optimize the feature similarity in a transferred latent domain formulated by utilizing a pre-trained autoencoder.
- Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal191
This work argues that, to remove reflection truly well, it should estimate the reflection and utilize it to estimate the background image, and proposes a cascade deep neural network, which estimates both the Background image and the reflection.
- 181
- Learning Deep Gradient Descent Optimization for Image Deconvolution120
A recurrent gradient descent network (RGDN) is proposed by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme and learns an implicit image prior and a universal update rule through recursive supervision.
- COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations109
A new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections is established and achieves state-of-the-art performance.
- 108
- RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion107
A light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks is introduced and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously.
- 85
- Part-Guided Attention Learning for Vehicle Instance Retrieval84
A Part Attention Module (PAM) is proposed to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights and achieves new state-of-the-art vehicle IR performance on four large-scale benchmark datasets.
- 84
- Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning73
Self-Expansion of pre-trained models with Modularized Adaptation (SEMA), a novel approach to enhance the control of stability-plasticity balance in PTM-based CL, enables better knowledge reuse and sub-linear expansion rate.
- Learning with Mixture of Prototypes for Out-of-Distribution Detection71
PrototypicAl Learning with a Mixture of prototypes (PALM) is proposed which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.
- Continual All-in-One Adverse Weather Removal With Knowledge Replay on a Unified Network Structure59
A novel continual learning framework with effective knowledge replay (KR) on a unified network structure is developed, which performs competitively to existing dedicated or joint training image restoration methods.
- Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning56
A Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers and achieves state-of-the-art result for mitigating forgetting.
- Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes53
A novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks is proposed, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other.
- Dual-Attention-Guided Network for Ghost-Free High Dynamic Range Imaging51
This work proposes a novel dual-attention-guided end-to-end deep neural network, called DAHDRNet, which produces high-quality ghost-free HDR images and employs a hybrid loss function, which consists of a perceptual loss, a total variation loss, and a content loss to recover photo-realistic images.
- Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation48
This paper proposes a memory-augmented network to learn and memorize the representative prototypes that cover diverse samples universally, and introduces a memory module to alleviate the forgetting issue by recording the patterns seen in mini-batch training.
- NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results45
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022, and the competition set-up, datasets, proposed methods and their results.
- 41
- Part-Guided Attention Learning for Vehicle Re-Identification.39
A Part Attention Module (PAM) is proposed to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights and achieves new state-of-the-art vehicle Re-ID performance on four large-scale benchmark datasets.
- High dynamic range imaging via gradient-aware context aggregation network38
The proposed HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain.
- DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling35
A plug-and-play training-free alignment method, DyMO, for aligning the generated images and human preferences during inference, with dynamic scheduling of multiple objectives and intermediate recurrent steps to reflect the requirements at different steps.
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 2026-10-14. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.