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- Enhanced Deep Residual Networks for Single Image Super-Resolution7,722
This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
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- Channel Attention Is All You Need for Video Frame Interpolation381
A simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component, and achieves outstanding performance compared to the existing models with a component for optical flow computation.
- Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning161
This work introduces a new meta-learning framework with a loss function that adapts to each task, named Meta-Learning with Task-Adaptive Loss Function (MeTAL), which demonstrates the effectiveness and the flexibility across various domains, such as few-shot classification and few- shot regression.
- Effectiveness of Gastric Cancer Screening on Gastric Cancer Incidence and Mortality in a Community-Based Prospective Cohort109
The findings of this study suggest that a decreased GC-specific mortality and improved survival rate in GC patients can be achieved through GE screening.
- Task-Aware Image Downscaling96
This paper proposes an auto-encoder-based framework that enables joint learning of the downscaling network and the upsc scaling network to maximize the restoration performance and validates the model’s generalization capability by applying it to the task of image colorization.
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- Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report69
The target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a realtime performance on mobile GPUs and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results.
- Machine learning-based predictive modeling of depression in hypertensive populations63
In conclusion, ML algorithms performed comparably in predicting depression among hypertensive populations and shed light on variables’ relative importance, paving the way for further clinical research.
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- DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks55
This work introduces a new ultra-low precision yet effective quantization approach specifically designed for SR, dubbed Distribution-Aware Quantization (DAQ), which manages to greatly reduce the computational and resource costs without the significant sacrifice in SR performance, compared to other quantization methods.
- Component-based LDA Face Descriptor for Image Retrieval33
A component-based face descriptor with LDA (Linear Discriminant Analysis) and a simple pose classification to deal with face image retrieval in huge database such as those in internet environments and experimental results on MPEG-7 database show an impressive accuracy.
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- Attentive Fine-Grained Structured Sparsity for Image Restoration25
This work proposes a novel pruning method that determines the pruning ratio for N:M structured sparsity at each layer of an image restoration network and demonstrates the efficacy of this method which outperforms previous pruning methods significantly.
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- AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results20
This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results, and employs the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
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- Batch Normalization Tells You Which Filter is Important13
This work shares the observation that the batch normalization (BN) parameters of pre-trained CNNs can be used to estimate the feature distribution of activation outputs, without processing of training data.
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- Searching for Controllable Image Restoration Networks9
A novel framework for controllable image restoration that can effectively restore multiple types and levels of degradation of a corrupted image, named TASNet, which is automatically determined by the neural architecture search algorithm, which optimizes the efficiency-accuracy trade-off of the candidate model architectures.
- Real-time object recognition and modeling for heavy-equipment operation8
The results show that the proposed method effectively recognizes construction objects, which could be used to enhance efficiency and productivity in the autonomous operation of heavy equipment.
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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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