Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks111 from public data
- Deep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors370
This paper proposes a Deep Dense Multi-Scale Network for snow removal by exploiting semantic and geometric priors, and designs a DDMSNet to learn semantic-aware and geometry-aware representation via a self-attention mechanism to produce the final clean images.
- 135
- 127
- On Estimating Air Pollution from Photos Using Convolutional Neural Network114
An effective convolutional neural network is devised to estimate air's quality based on photos to alleviate the vanishing gradient issue effectively and a modified activation function is developed for photo based air pollution estimation.
- Sparse representation for robust abnormality detection in crowded scenes111
This paper proposes a fully unsupervised non-negative sparse coding based approach for abnormality event detection in crowded scenes, which is specifically tailored to cope with feature noisy and uncertainty.
- Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering104
A new framework called Self-supervised Information Bottleneck based Multi-view Subspace Clustering (SIB-MSC) is established, which can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representation of other views.
- On modeling and predicting individual paper citation count over time97
This work proposes a method for predicting the citation counts of individual publications, over an arbitrary time period, and explores paper-specific covariates and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively.
- 94
- Residual Invertible Spatio-Temporal Network for Video Super-Resolution93
A novel end-to-end architecture called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution that can sufficiently exploit the spatial information from low-resolution to high-resolution, and effectively models the temporal consistency from consecutive video frames.
- Self-Paced Multi-Task Learning91
This paper proposes a novel multi-task learning framework that attempts to learn the tasks by simultaneously taking into consideration the complexities of both tasks and instances per task, by presenting a new task-oriented regularizer that can jointly prioritize tasks and instance.
- Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior78
A Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information is presented, and a Semantic-Aware Deraining Module (SADM) is developed which solves both tasks of semantic segmentation and deraining of scenes.
- On Scalability of Association-rule-based Recommendation71
A distributed framework that can scale different association-rule-based recommendation methods in a unified way is presented, where a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores.
- Autoencoder Regularized Network For Driving Style Representation Learning71
Experiments show that ARNet can learn a good generalized driving style representation and significantly outperforms existing methods and alternative architectures by reaching the least estimation error and the highest identification accuracy compared with traditional supervised learning methods.
- Learning Transferable Self-Attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision68
A novel weakly supervised framework to simultaneously locate action frames as well as recognize actions in untrimmed videos, which takes advantage of the self-attention mechanism to weight each frame, such that the influence of background frames can be effectively eliminated.
- AdapNet: Adaptability Decomposing Encoder–Decoder Network for Weakly Supervised Action Recognition and Localization60
A novel adaptability decomposing encoder–decoder network is proposed to transfer reliable knowledge between the trimmed and untrimmed videos for action recognition and localization by bidirectional point process modeling, given only video-level annotations.
- FreeKD55
This paper proposes the first Free-direction Knowledge Distillation framework via Reinforcement learning for GNNs, called FreeKD, which is no longer required to provide a deeper well-optimized teacher GNN, and has comparable or even better performance than traditional KD algorithms that distill knowledge from a deeper and stronger teacher Gnn.
- 55
- 46
- Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score45
This paper proposes a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations, and theoretically proves that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions.
- 45
- Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning44
This paper aims to improve MLE by discriminative and adversarial learning by devising a gradient based learning procedure with two complementary recipes that directly reflects the prediction accuracy of the model and can add sharpness to the smooth effect inherently caused by the MSE loss.
- A Self-Paced Regularization Framework for Multilabel Learning42
This work proposes a new multilabel learning formulation by introducing a self-paced function as a regularizer, so as to simultaneously prioritize label learning tasks and instances in each iteration, and provides a general way to find the desiredSelf-paced functions.
- Multi-Instance Multi-Label Action Recognition and Localization Based on Spatio-Temporal Pre-Trimming for Untrimmed Videos39
A novel multi-instance multi-label modeling network based on spatio-temporal pre-trimming to recognize actions and locate corresponding frames in untrimmed videos and demonstrates the efficacy of this method when compared with the state-of-the-arts.
- Reconstruction Regularized Deep Metric Learning for Multi-Label Image Classification.39
A novel deep metric learning method to tackle the multi-label image classification problem by attempting to explore a latent space, where images and labels are embedded via two unique deep neural networks, respectively, to capture the relationships between image features and labels.
- Similarity Learning via Kernel Preserving Embedding39
It is argued that it is beneficial to preserve the overall relations when the authors extract similarity information and proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.
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 2026-10-15. 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.
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.