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.
Works32 from public data
- NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results106
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results, based on the SIDD benchmark.
- QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results87
A score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation is evaluated, confirming the importance and complementary value that uncertainty estimates provide to segmentation algorithms.
- NTIRE 2022 Challenge on Perceptual Image Quality Assessment84
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022 to address the emerging challenge of IQA by perceptual image processing algorithms.
- ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images83
The ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models in the Automatic Detection challenge on Age-related Macular degeneration (ADAM).
- AIM 2020 Challenge on Rendering Realistic Bokeh52
The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.
- 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.
- 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.
- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
- 36
- Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment35
A new multivariate regression based convolutional neural network model (CNN) model is proposed to predict the fundus image quality, achieving a strong correlation with the subjective scores.
- Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image34
An end-to-end Deep Multi-Scale Hierarchical Network (DMSHN) model is used for direct Bokeh effect rendering of images captured from the monocular camera with around 6x less runtime compared to the current state-of-the-art model in processing HD quality images.
- 33
- Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report28
The first Mobile AI challenge is introduced, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
- 26
- Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling18
This paper explores an efficient solution for Spacetime Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos, and uses a refinement network to improve the quality of HR intermediate frame via residual learning.
- Brain Tumor Segmentation with Cascaded Deep Convolutional Neural Network17
This study has proposed fully automatic two step approach for Glioblastoma (GBM) brain tumor segmentation with Cascaded U-Net and results are validated on 125 patients.
- Anomaly Detection In Retinal Images Using Multi-Scale Deep Feature Sparse Coding16
This work proposed a simple, memory efficient, easy to train method which followed a multi-step training technique that incorporated autoencoder training and Multi-Scale Deep Feature Sparse Coding (MDFSC), an extended version of normal sparse coding, to accommodate diverse types of retinal datasets.
- Colorectal Cancer Segmentation Using Atrous Convolution and Residual Enhanced UNet14
This work proposes another CNN-based approach, which uses atrous convolutions and residual connections besides the conventional filters for colorectal cancer segmentation, trained on the DigestPath 2019 Challenge dataset.
- GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos11
This paper proposes a vision transformer-based approach to jointly learn spatio-temporal features directly from sequence of frame-level patches that intelligently combines short-term and long-term spatio -temporal feature representations.
- 7
- 5
- 4
- 3
- Cinéaste: A Fine-grained Contextual Movie Question Answering Benchmark2
The analysis reveals that long-range temporal reasoning is a primary bottleneck, with the top open-source model achieving only 63.15\% accuracy, which underscores significant challenges in fine-grained contextual understanding and the need for advancements in long-form movie comprehension.
- 1
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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.