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
- Tien‐Tsin WongSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works119 from public data
- R³Net: Recurrent Residual Refinement Network for Saliency Detection522
A novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image that outperforms competitors in all the benchmark datasets.
- SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection341
A scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales is presented and a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects is presented.
- Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection285
This paper presents a network to detect shadows by exploring and combining global context in deep layers and local context in shallow layers of a deep convolutional neural network (CNN) and develops a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers.
- Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection241
This work presents a Semantic Context based Anomaly Detection Network, SCADN, for unsupervised anomaly detection by learning the semantic context from the normal samples by generating multi-scale striped masks to remove a part of regions from thenormal samples and training a generative adversarial network to reconstruct the unseen regions.
- Deep Texture-Aware Features for Camouflaged Object Detection165
Experimental results show that the approach outperforms various state-of-the-art methods by a large margin, and the network is evaluated on the benchmark datasets for camouflaged object detection both qualitatively and quantitatively.
- 138
- Deep Attentional Features for Prostate Segmentation in Ultrasound123
A novel deep neural network equipped with deep attentional feature (DAF) modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN).
- Hybrid dual Kalman filtering model for short‐term traffic flow forecasting112
A novel hybrid dual Kalman filter is presented for accurate and timely short-term traffic flow forecasting and outperforms the state-of-the-art parametric and non-parametric models.
- δ -agree AdaBoost stacked autoencoder for short-term traffic flow forecasting102
An adaptive boosting scheme is proposed to ensemble the trained stacked autoencoders to improve the accuracy of traffic flow forecasting and sufficient experiments have been conducted to demonstrate the superior performance of the proposal.
- 68
- Structure-based ASCII art68
This paper presents a novel method to generate structure-based ASCII art that approximates the major line structure of the reference image content with the shape of characters using a novel alignment-insensitive shape similarity (AISS) metric.
- A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting66
A novel deep learning-based multimodel integration framework in order to overcome the limitations of previous methods in dealing with large variations and uncertainties of traffic flow and hence improve the forecasting accuracy.
- SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting64
This study proposes a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model, whereby the GSA is employed to search optimal SVR parameters.
- Context-Aware and Scale-Insensitive Temporal Repetition Counting63
A context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown and diverse cycle-lengths, and proposes a bidirectional cycle length estimation method for a context- aware prediction.
- Animating animal motion from still62
This paper infer motion cycle of animals from the "motion snapshots" (snapshots of different individuals) captured in a still picture to demonstrate the applicability of the proposed method to a wide range of species, including birds, fishes, mammals, and reptiles.
- 55
- 52
- 52
- G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors42
G2Face is introduced, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility.
- 42
- From Continuity to Editability: Inverting GANs with Consecutive Images41
This paper introduces consecutive images into the inversion process, providing the first support of video-based GAN inversion and an interesting application of unsupervised semantic transfer from consecutive images.
- 37
- Rolling normal filtering for point clouds36
Compared with other state-of-the-art denoising methods, this approach is more robust in removing small-scale geometric features while retaining large-scale structures and exhibits superiority in preventing large- scale sharp structures from severe distortion.
- 32
- D4Net: De-deformation defect detection network for non-rigid products with large patterns28
A D4Net is proposed to detect defects of a non-rigid product with deformation in a given image and its corresponding reference image by focusing on differences between high-level semantic features extracted from the deep neural network to emphasize the region of possible defects.
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.