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- David D. ZhangSuggested from co-authorship
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Works76 from public data
- Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression5,440
A Distance-IoU (DIoU) loss is proposed by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses, thereby leading to faster convergence and better performance.
- Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation1,511
This article proposes complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding-box regression and nonmaximum suppression, leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
- Progressive Image Deraining Networks: A Better and Simpler Baseline1,052
A better and simpler baseline deraining network by considering network architecture, input and output, and loss functions is provided and is expected to serve as a suitable baseline in future deraining research.
- Neural Blind Deconvolution Using Deep Priors347
Experimental results show that the proposed SelfDeblur can achieve notable quantitative gains as well as more visually plausible deblurring results in comparison to state-of-the-art blind deconvolution methods on benchmark datasets and real-world blurry images.
- Localization Distillation for Dense Object Detection208
This paper reformulates the knowledge distillation process on localization to present a novel localization distillation method which can efficiently transfer the localization knowledge from the teacher to the student and heuristically introduces the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region.
- Unpaired Learning of Deep Image Denoising197
A two-stage scheme to facilitate unpaired learning of denoising network by incorporating self-supervised learning and knowledge distillation is presented, which performs favorably on both synthetic noisy images and real-world noisy photographs.
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- Localization Distillation for Object Detection102
This paper presents a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student and introduces the concept of valuable localization region that can aid to selectively distill the classification and localization knowledge for a certain region.
- Simultaneous Fidelity and Regularization Learning for Image Restoration96
A principled algorithm within the maximum a posterior framework to tackle image restoration with a partially known or inaccurate degradation model is proposed, which demonstrates the effectiveness of the proposed model for image deconvolution with inaccurate blur kernels, deconVolution with multiple degradations and rain streak removal.
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- Two-stage single image reflection removal with reflection-aware guidance62
A novel two-stage network with reflection-aware guidance (RAGNet) for single image reflection removal (SIRR) and demonstrates the quantitative and qualitative superiority of the RAGNet in comparison to the state-of-the-art SIRR methods.
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- Fast total-variation based image restoration based on derivative alternated direction optimization methods47
This paper develops a novel derivative space-based reformulation together with an efficient derivative alternating direction method of multipliers (D-ADMM) algorithm, which can obtain satisfactory restoration result and is much faster than the state-of-the-art TVIR algorithms.
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- NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results41
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video and proposed the LDV 2.0 dataset, which includes theLDV dataset (240 videos) and 95 additional videos.
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- Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment35
This paper proposes to incorporate semi-supervised and positive-unlabeled (PU) learning for exploiting unlabeled data while mitigating the adverse effect of outliers in full-reference image quality assessment.
- What Deep CNNs Benefit From Global Covariance Pooling: An Optimization Perspective31
This paper explores the effect of GCP on deep CNNs in terms of the Lipschitzness of optimization loss and the predictiveness of gradients, and shows that GCP can make the optimization landscape more smooth and the gradients more predictive.
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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 2026-10-13. 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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