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Works196 from public data
- Deep Back-Projection Networks for Super-Resolution1,598
It is shown that extending the idea to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct further improve super-resolution, yielding superior results and in particular establishing new state of the art results for large scaling factors such as 8× across multiple data sets.
- Recurrent Back-Projection Network for Video Super-Resolution553
A novel architecture for the problem of video super-resolution that integrates spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super- resolution path for the target frame.
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- Task-Driven Super Resolution: Object Detection in Low-Resolution Images226
A novel framework to train a deep neural network where the SR sub-network explicitly incorporates a detection loss in its training objective, via a tradeoff with a traditional detection loss is proposed.
- Space-Time-Aware Multi-Resolution Video Enhancement126
The components of the model that generate latent low- and high-resolution representations during ST-SR can be used to finetune a specialized mechanism for just spatial or just temporal super-resolution.
- Detecting Dementia Through Interactive Computer Avatars126
A new computer avatar with spoken dialog functionalities that produces spoken queries based on the mini-mental state examination, the Wechsler memory scale-revised, and other related neuropsychological questions and has the potential to detect dementia through spoken dialog systems and can assist health care workers.
- Real-time multitarget tracking by a cooperative distributed vision system123
This paper categorizes target tracking systems based on characteristics of scenes, tasks, and system architectures, and presents a real-time cooperative multitarget tracking system that can track multiple moving objects persistently even under complicated dynamic environments in the real world.
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- Deep Back-ProjectiNetworks for Single Image Super-Resolution92
This work proposes Deep Back-Projection Networks (DBPN), the winner of two image super-resolution challenges, that exploit iterative up- and down-sampling layers that represent different types of low- and high-resolution components.
- Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction67
Experimental results showed the new normalizing flow-based trajectory prediction model named FlowChain achieved state-of-the-art trajectory prediction accuracy and demonstrated superiority in the accuracy and speed of density estimation.
- Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth63
The authors have created an AI system with a confidence score that is useful for non-invasive selection of embryos that could result in live birth and successfully visualized embryo features in focused areas that had potential to distinguish between live and non-live births.
- NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results54
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single image super- resolution.
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- NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results43
The NTIRE2021 challenge on burst super-resolution was to generate a clean RGB image with 4 times higher resolution given a RAW noisy burst as input, and the top-performing methods set a new state-of-the-art for the burstsuper-resolution task.
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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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