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- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
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- Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning36
This paper proposes a novel framework named Unified Data-Free Compression (UDFC), which performs pruning and quantization simultaneously without any data and fine-tuning process, and achieves significant improvements over various network architectures and compression methods.
- Selective Learning Confusion Class for Text-Based CAPTCHA Recognition36
A novel method of selective learning confusion class for text-based CAPTCHA recognition with a view on improving the recognition accuracy of the confusing characters in confusion-class DCNN is presented.
- A Learning Framework for n-Bit Quantized Neural Networks Toward FPGAs35
To solve the gradient vanishing problem, this article proposes a reconstructed gradient function for QNNs in the back-propagation algorithm that can directly get the real gradient rather than estimating an approximate gradient of the expected loss.
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- NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results29
This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.
- Low-Light Image Enhancement via Generative Perceptual Priors24
A novel LLIE framework with the guidance of Generative Perceptual Priors (GPP-LLIE) derived from vision-language models (VLMs) is presented and it is demonstrated that this model outperforms current SOTA methods on paired LL datasets and exhibits superior generalization on real-world data.
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- Ntire 2025 Image Shadow Removal Challenge Report22
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge, with 17 teams successfully submitting their solutions during the final evaluation phase, and one focusing on reconstruction fidelity and the other on visual perception through a user study.
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- Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey21
The NTIRE 2024 RAW Image Super-Resolution Challenge is reviewed, highlighting the proposed solutions and results, and providing a gauge for the current state-of-the-art in RAW Image Super-Resolution.
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- Research on improved algorithm of object detection based on feature pyramid20
Compared with SSD, the accuracy of FP-SSD is higher, which has more accurate location and higher recognition confidence, and the algorithm applied the deconvolution network to the high-level features of the feature pyramid to get the semantic information and convolution for the middle level features to reduce the feature channels.
- 3QNet18
A novel learning-based point cloud compression framework named 3D Point Cloud Geometry Quantiation Compression Network (3QNet) is proposed, which overcomes the robustness limitation of existing point-based methods and can handle dense points.
- Learning Global-aware Kernel for Image Harmonization16
A novel Global-aware Kernel Net-work (GKNet) is proposed to harmonize local regions with comprehensive consideration of long-distance background references for local harmonization and a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization.
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- Out-of-Stock Detection Based on Deep Learning13
An out-of-stock detection method based on deep learning based on canny operator performs better, which can achieve a recall rate of 83.9%, a precision rate of 91.7% and a F1-Measure rate of 87.6% in real scene dataset.
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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