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Works37 from public data
- Deep Back-Projection Networks for Super-Resolution1,599
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.
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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.
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- First-order derivative-based super-resolution30
Experimental results indicate that the proposed fast super-resolution method based on first-order derivatives from neighbor pixels clearly outperforms other state-of-the-art algorithms such as fast curvature-based interpolation.
- Inception learning super-resolution19
Experimental results demonstrate that the proposed ILSR network can construct sharp edges and clean textures, and reduce computation time by up to three orders of magnitude compared to state-of-the-art methods.
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- Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks11
This paper proposes a multi-stream network merging hand and hand-location features, which help to discriminate target gestures from natural movements of the hand, since these may not happen in the same 3D spatial location.
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- Hand gesture recognition with deep residual network using Semg signal5
The proposed model for hand gesture recognition using the SDTO_DRN model is accurate and improves the effectiveness of the recognition.
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- Improving Classification Performance on Imbalanced Medical Data using Generative Adversarial Network2
The oversampling-based Generative Adversarial Networks method is explored to improve the performance of the classification algorithm over imbalanced medical datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.
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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 2026-10-14. 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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