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- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
- NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results83
This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained, and offers a comprehensive overview of current trends in this field.
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- A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining49
A new coarse-to-fine single image deraining framework termed Multi-stream Hybrid Deraining Network (shortly, MH-DerainNet) is proposed, which combines the SSIM loss and perceptual loss to preserve the per-pixel similarity as well as preserving the global structures so that the deraining result is more accurate.
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
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- Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition37
A novel Motion-guided Modulation Network (MMN) is presented that implicitly captures and modulates subtle motion cues to enhance spatial-temporal representation learning and achieves state-of-the-art performance in skeleton-based micro-action recognition.
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- SGINet: Toward Sufficient Interaction Between Single Image Deraining and Semantic Segmentation35
A Semantic Guided Interactive Network (SGINet), which considers the sufficient interaction between SID and semantic segmentation using a three-stage deraining manner, i.e., coarse deraining, semantic information extraction, and semantics guided deraining.
- The Tenth NTIRE 2025 Image Denoising Challenge Report34
An overview of the NTIRE 2025 Image Denoising Challenge is presented, highlighting the proposed methodologies and corresponding results, providing insights into the current state-of-the-art in image denoising.
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- Temporal-Frequency State Space Duality: An Efficient Paradigm for Speech Emotion Recognition29
TF-Mamba, a novel multi-domain framework that captures emotional expressions in both temporal and frequency dimensions, achieves an optimal balance between computational efficiency and model expressiveness for Speech Emotion Recognition (SER).
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- Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal27
This paper proposes a new and universal SID model with novel modules, termed Robust Attention Deraining Network (RadNet), with strong robustness and generalization ability, and demonstrates that this RadNet could outperform other state-of-the-art SID methods.
- Raw Image Reconstruction From Rgb on Smartphones. Ntire 2025 Challenge Report23
This paper aims to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata and, by doing this, “reverse” the ISP transformation.
- Micro-gesture Online Recognition using Learnable Query Points23
The solution developed by the team, HFUT-VUT, for the Micro-gesture Online Recognition track in the MiGA challenge at IJCAI 2024 ranks 2nd in the Micro-gesture Online Recognition track.
- 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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- Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition20
This approach, built on a multi-dimensional Video Swin Transformer model, leverages this ensemble strategy to achieve competitive performance, and ranked 3rd in both the RGB-based ISLR and RGB-D-based ISLR tracks, demonstrating the effectiveness in handling the challenges of cross-view recognition.
- MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results18
The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal and a detailed description of all models developed is provided in this paper.
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- Fuel Cell Virus Sensor Using Virus Capture within Antibody-Coated Nanochannels16
A unique fuel cell sensor system for the first time direct detection of unlabeled virus particles based on the formation of antibody-virus complexes within the sensor's membrane nanochannels offers the outstanding promise of a sustainable, low-cost, and rapid low-power virus detection tool.
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- DerainCycleGAN: An Attention-guided Unsupervised Benchmark for Single Image Deraining and Rainmaking.14
This paper designs an unsu-pervised attention guided rain streak extractor that utilizes a memory to extract the rain streak masks with two constrained cycle-consistency branches jointly by paying attention to both the rainy and rain-free image domains and proposes a novel net called Attention-guided Deraining by Constrained CycleGAN.
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- XInsight: Integrative Stage-Consistent Psychological Counseling Support Agents for Digital Well-Being11
Experiments show improved paradigm alignment, multi-therapy integration, interaction depth, and interpretability over existing multi-agent counseling systems, indicating that XInsight provides a practical blueprint for integrating counseling-inspired support agents into web applications for digital well-being.
- Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal11
CyclicPrompt is proposed, an innovative cyclic prompt approach designed to enhance the effectiveness, adaptability, and generalizability of UAWR, and adeptly harnesses weather-specific knowledge, textual contexts, and reliable textures.
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- Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement8
The case studies demonstrate that Clarity ChatGPT effectively improves the generalization and interaction capabilities in the IRE, and also fills the gap in the low-level domain of the existing vision-language model.
- Temporal Boundary Awareness Network for Repetitive Action Counting7
This work constructs a boundary-aware encoder equipped with a temporal pyramid structure to build multi-scale video features, capturing the period information of different lengths of repetitive actions to solve the period inconsistency problem.
- DerainCycleGAN: A Simple Unsupervised Network for Single Image Deraining and Rainmaking7
This paper designs an unsu-pervised attention guided rain streak extractor that utilizes a memory to extract the rain streak masks with two constrained cycle-consistency branches jointly by paying attention to both the rainy and rain-free image domains and proposes a novel net called Attention-guided Deraining by Constrained CycleGAN.
- Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search4
The HFUT-LMC team's solution to the WWW 2025 challenge on Text-based Person Anomaly Search is presented, and the Similarity Coverage Analysis (SCA) strategy is introduced to address the recognition difficulty caused by similar text descriptions.
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- Comprehensive Survey on Person Identification: Queries, Methods, and Datasets3
The datasets commonly employed in person re-identification research are explored, which are critical for training and evaluating models under both single-modal and multi-modal scenarios.
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- From Outline to Detail: An Hierarchical End-to-end Framework for Coherent and Consistent Visual Novel Generation and Assembly2
A hierarchical end-to-end framework for automatic VN generation and assembly is proposed, which employs an outline-guided autoregressive generation mechanism that transforms high-level user prompts into coherent plots, while a vision LLM-based self-correction mechanism ensures consistency between multimedia assets and plot content.
- Repetitive Action Counting with Feature Interaction Enhancement and Adaptive Gate Fusion2
This work proposes a feature interaction enhancement module designed to improve the learning of action-relevant features from both RGB and skeleton data, and introduces an adaptive local-global attention gating fusion mechanism to integrate enhanced processed RGB and skeleton features.
- Image Data Augmentation Method based on Style Transfer2
An efficient method for image data augmentation that is mostly based on style transfer is suggested that can reduce overfitting and show a rather satisfactory performance on the authors' self-made dataset.
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- Visual saliency landmark detection algorithm based on DNN feature extraction1
The algorithm of visual saliency landmark detection for scene matching based on deep neural network for feature extraction is proposed and illustrated that the algorithm can effectively solve the problem of landmark detection in different environments and improve the robustness and effectiveness of landmark Detection.
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- SWSD: An Abnormal Detection Algorithm on Unequally Spaced Time Series for Disaster Prediction–
The main contribution of this paper is to define the concept of optimal interval for unequally spaced series, and propose an algorithm, called SWSD, which can be used in unequally spacing time series abnormal detection for disaster prediction.
- Community Detecting by Signal and Flooding Algorithm on Complex Networks–
In order to get the feasible spatial vectors, this paper puts forward the optimum passes method, which allows the reduction of the searching space and speeds up the convergence of algorithm.
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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 2026-10-15. 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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