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- Adversarial Cross-Modal Retrieval781
Comprehensive experimental results show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.
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- Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval479
A novel cross- modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints, and an effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes.
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- Deep adversarial metric learning for cross-modal retrieval217
A novel Deep Adversarial Metric Learning approach, termed DAML for cross-modal retrieval, which introduces a modality classifier to predict the modality of a transformed feature, which ensures that the transformed features are also statistically indistinguishable.
- Wound intensity correction and segmentation with convolutional neural networks215
A fast level set model‐based method for intensity inhomogeneity correction and a spectral properties‐based color correction method to overcome obstacles in the wound healing process.
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- Binary Generative Adversarial Networks for Image Retrieval206
This paper uses binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way and significantly outperforms existing hashing methods by up to 107% in terms of mAP.
- Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking177
This work proposes a novel Multi-modal Tensor Fusion Network (MTFN) to explicitly learn an accurate image-text similarity function with rank-based tensor fusion rather than seeking a common embedding space for each image- text instance.
- Template-Based Math Word Problem Solvers with Recursive Neural Networks168
This paper designs a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner, and establishes the superiority of the new framework as it improves the accuracy by a wide margin in two of the largest datasets.
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- MSFlow: Multiscale Flow-Based Framework for Unsupervised Anomaly Detection116
A novel multiscale flow-based framework (MSFlow) composed of asymmetrical parallel flows followed by a fusion flow to exchange multiscale perceptions is proposed, significantly outperforming existing methods for anomaly detection and localization.
- Universal Weighting Metric Learning for Cross-Modal Matching110
A simple and interpretable universal weighting framework for cross-modal matching is proposed, which provides a tool to analyze the interpretability of various loss functions and introduces a new polynomial loss under the universal weighted framework.
- Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction109
A novel deep-sea imaging method that compensates for the attenuation discrepancy along the propagation path, and an effective underwater scene enhancement scheme are described, characterized by a reduced noise level, better exposure of dark regions, and improved global contrast such that the finest details and edges are significantly enhanced.
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- From General to Specific: Informative Scene Graph Generation via Balance Adjustment99
BA-SGG, a simple yet effective SGG framework based on balance adjustment but not the conventional distribution fitting, integrates two components: Semantic Adjustment (SA) and Balanced Predicate Learning (BPL) respectively for adjusting these imbalances.
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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 2026-10-12. 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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