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- Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision616
A neural network containing three parallel branches to capture different relations among video snippets and integrate features, where holistic branch captures long-range dependencies using similarity prior, localized branch captures local positional relation using proximity prior, and score branch dynamically captures the closeness of predicted score.
- HVI: A New Color Space for Low-light Image Enhancement244
A new color space for LLIE is proposed, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity, defined by polarized HS maps and learnable intensity.
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- ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation154
This paper proposes an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism, and demonstrates the effectiveness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.
- Video Event Restoration Based on Keyframes for Video Anomaly Detection128
A novel U-shaped Swin Transformer Network with Dual Skip Connections (USTN-DSC) for video event restoration, where a cross-attention and a temporal upsampling residual skip connection are introduced to further assist in restoring complex static and dynamic motion object features in the video.
- Open-Vocabulary Video Anomaly Detection123
This paper proposes a model that decouples OVVAD into two mutually comple-mentary tasks - class-agnostic detection and class-specific classification - and jointly optimizes both tasks, and designs a semantic knowledge injection module and novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task.
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- Fast sparse coding networks for anomaly detection in videos105
A Fast Sparse Coding Network (FSCN) based on High-level Features is proposed, which is hundreds of or even thousands of times faster at the test stage and reaches the state-of-the-art level.
- Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts96
A novel method called STPrompt that learns spatio-temporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs) achieves state-of-the-art performance on three public benchmarks for the WSVADL task.
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- HANet79
The proposed Hierarchical Alignment Network (HANet) to align different level representations for video-text matching outperforms other state-of-the-art methods, which demonstrates the effectiveness of hierarchical representation and alignment.
- Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model74
A novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios, and introduces an efficient pretext task to enhance semantic associations between video-text fine-grained representations.
- Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images58
A foreground and background separation X-ray prohibited item detection framework, which separates prohibited items from other items to exclude irrelevant information and significantly outperforms state-of-the-art solutions.
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- Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition41
Inspired by the notion that high/low frequency information is applicable to different degradations, HLNet is introduced, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition, thus achieving higher-quality image restoration.
- Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio41
An improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio, based on the Inception-ResNet network.
- CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task40
The Composite Refinement Network (CRNet) is proposed, which can perform unified image restoration and enhancement on multiple exposure images using multiple exposure images, and explicitly separates and strengthens high and low-frequency information through pooling layers, using specially designed Multi-Branch Blocks.
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- Bidirectional Retrospective Generation Adversarial Network for Anomaly Detection in Videos37
A novel future frame prediction model based on a bidirectional retrospective generation adversarial network (BR-GAN) for anomaly detection that outperforms most existing state-of-the-art methods and a sequence discriminator composed of a 3D convolutional neural network to capture the long-term temporal relationships between frame sequences composed of predicted frames and input frames.
- Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model36
The deep-convolutional model can accurately detect stent struts in IVOCT images, thus enabling the fully-automatic quantification of stent parameters in an extremely short time, and might facilitate the application of quantitative IVO CT analysis in real-world clinical scenarios.
- NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results30
A comprehensive overview of the NTIRE 2025 Efficient Burst HDR and Restoration Challenge is provided, the proposed solutions are compared, and it serves as a valuable reference for researchers and practitioners in efficient burst HDR and restoration.
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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