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- ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows272
ArtFlow is proposed to prevent content leak during universal style transfer using reversible neural flows and an unbiased feature transfer module that supports both forward and backward inferences and operates in a projection-transfer-reversion scheme.
- AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators222
The proposed Automated Graph Contrastive Learning framework employs a set of learnable graph view generators orchestrated by an auto augmentation strategy, resulting in topological heterogeneity yet semantic similarity in the generation of contrastive samples.
- ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal196
A unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal and progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation.
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- A multimodal visual–language foundation model for computational ophthalmology92
This work proposes EyeCLIP, a multimodal visual-language foundation model trained on 2.77 million ophthalmology images from 11 modalities with partial clinical text, which offers significant potential for detecting both ocular and systemic diseases, and bridging gaps in real-world clinical applications.
- Semi-Supervised Active Learning with Temporal Output Discrepancy92
A novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss is presented, which achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
- ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement72
A novel deep reinforcement learning based method, dubbed ReLLIE, for customized low-light enhancement, which models LLIE as a markov decision process, i.e., estimating the pixel-wise image-specific curves sequentially and recurrently.
- TP-LSD: Tri-Points Based Line Segment Detector71
This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed, and introduces the tri-points representation, converting the line segment detection to the end-to-end prediction of a root-point and two endpoints for each line segment.
- ShadowFormer: Global Context Helps Image Shadow Removal67
A Retinex-based shadow model is proposed, from which a novel transformer-based network is derived, dubbed ShandowFormer, to exploit non-shadow regions to help shadow region restoration and achieves state-of-the-art performance by using up to 150X fewer model parameters.
- SBAT: Video Captioning with Sparse Boundary-Aware Transformer64
A novel method called sparse boundary-aware transformer (SBAT) is proposed to reduce the redundancy in video representation and an aligned cross-modal encoding scheme to boost the multimodal interaction.
- Deep Learning Driven Visual Path Prediction From a Single Image64
A deep learning framework is proposed, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling and a unified path-planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models.
- GNAS61
This work proposes an efficient greedy neural architecture search approach (GNAS) to automatically discover the optimal tree-like deep architecture for multi-attribute learning and demonstrates the effectiveness and compactness of neural architectures derived by GNAS.
- Mechanism of Phosgene-Induced Acute Lung Injury and Treatment Strategy60
The literature on the mechanism and treatment strategies to explore new ideas for the treatment of phosgene poisoning are reviewed to ensure there is currently no specific antidote for phosGene poisoning.
- Semi-supervised co-training and active learning based approach for multi-view intrusion detection58
This paper proposes a co-training method framework for intrusion detection, which is a semi-supervised learning method and can not only utilize unlabeled data, but can also combine multi-view data.
- TVT: Two-View Transformer Network for Video Captioning.57
A novel video captioning framework, i.e., Two-View Transformer (TVT), which comprises of a backbone of Transformer network for sequential representation and two types of fusion blocks in decoder layers for combining different modalities effectively.
- QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity54
A new style transfer framework for high visual-fidelity stylization called QunntArt, which fusing the quantized and continuous latent representations of the generated artwork toward the centroids of the real artwork distribution with vector quantization.
- Text Guided Person Image Synthesis51
It shows for the first time that the person image can automatically edit from the natural language descriptions, and synthesizes a realistic and appearance transferred person image according to the text in conjunction with the target pose.
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- MicroRNAs: Important Regulatory Molecules in Acute Lung Injury/Acute Respiratory Distress Syndrome49
The miRNAs involved in the inflammatory process of ALi/ARDS are reviewed to provide new ideas for the pathogenesis, clinical diagnosis, and treatment of ALI/ARDS.
- Research on the identification method for the forest fire based on deep learning45
The experimental results show that the forest fire image recognition method proposed in this paper can obtain higher recognition rate and lower false alarm rate after training with fewer samples than other algorithms, and has obvious advantages over algorithms that require GPU training environment.
- Fast-flux service network detection based on spatial snapshot mechanism for delay-free detection45
This study proposes a delay-free detection system, Spatial Snapshot Fast-flux Detection system (SSFD), capable to capture the geographical pattern of hosts as well as mapping IP addresses in a DNS response into geographic coordinate system for revealing FFSNs at the moment.
- BM-NAS: Bilevel Multimodal Neural Architecture Search43
The proposed Bilevel Multimodal Neural Architecture Search (BM-NAS) framework, which makes the architecture of multimodal fusion models fully searchable via a bilevel searching scheme, achieves competitive performances with much less search time and fewer model parameters in comparison with the existing generalized multimodals NAS methods.
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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 2026-10-13. 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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