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- Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization592
A new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments is proposed, which indicates the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.
- Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval495
A self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self- supervised fashion, to maximize the semantic correlation and consistency of the representations between different modalities.
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- Triplet-Based Deep Hashing Network for Cross-Modal Retrieval412
A triplet-based deep hashing (TDH) network for cross-modal retrieval using the triplet labels, which describe the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross- modal instances.
- Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation385
A novel Multi-task Collaborative Network (MCN) is proposed to achieve a joint learning of REC and RES for the first time and addresses a key challenge in this multi-task setup, i.e., the prediction conflict.
- Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval344
This paper proposes a novel deep cross-modal hashing method to generate compact hash codes through an end-to-end deep learning architecture, which can effectively capture the intrinsic relationships between various modalities.
- Deep Spectral Clustering Using Dual Autoencoder Network328
A joint learning framework for discriminative embedding and spectral clustering is proposed, which can significantly outperform state-of-the-art clustering approaches and be more robust to noise.
- Graph Debiased Contrastive Learning with Joint Representation Clustering227
This work proposes a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering, and randomly selects negative samples from the clusters which are different from the positive sample's cluster.
- A Short Survey of Recent Advances in Graph Matching223
The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.
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- Semantic Structure-based Unsupervised Deep Hashing176
This work designs a deep architecture and a pair-wise loss function to preserve the semantic structure of the semantic relationships between points in unsupervised settings, and shows that SSDH significantly outperforms current state-of-the-art methods.
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- DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs169
A new deep unsupervised hashing model, called DistilHash, is proposed, which can learn a distilled data set, where data pairs have confident similarity signals and the semantic similarity labels assigned by the optimal Bayesian classifier can be potentially distilled.
- Active Transfer Learning Network: A Unified Deep Joint Spectral–Spatial Feature Learning Model for Hyperspectral Image Classification168
A unified deep network, combined with active transfer learning (TL) that can be well-trained for HSIs classification using only minimally labeled training data and which significantly outperforms many state-of-the-art approaches on three popular data sets.
- Robust Reversible Watermarking via Clustering and Enhanced Pixel-Wise Masking165
This paper presents a novel pragmatic framework, wavelet-domain statistical quantity histogram shifting and clustering (WSQH-SC), which ingeniously constructs new watermark embedding and extraction procedures by histogram shift-based and histogram clustering, which are important for improving robustness and reducing run-time complexity.
- A local Tchebichef moments-based robust image watermarking164
This paper proposes a content-based watermarking scheme that combines the invariant feature extraction with watermark embedding by using Tchebichef moments to realize the robustness to common image processing operations and the blind detection.
- Adaptive-weighting discriminative regression for multi-view classification161
This work fuse the multi-view data by mapping the concatenation of original features to a discriminative low-dimensional subspace, where the features from different views are adaptively assigned with the learned optimal weights.
- Learning A Structured Optimal Bipartite Graph for Co-Clustering159
A novel co-clustering method to learn a bipartite graph with exactly k connected components, where k is the number of clusters, which approximates the original graph but maintains an explicit cluster structure.
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- Shared Predictive Cross-Modal Deep Quantization157
A deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search, and can preserve intramodal and intermodal similarities as much as possible and greatly reduce quantization error.
- Active multi-kernel domain adaptation for hyperspectral image classification151
A novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL) based on utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain.
- Geometric Distortion Insensitive Image Watermarking in Affine Covariant Regions150
A geometrically invariant image watermarking based on affine covariant regions (ACRs) that provides a certain degree of robustness and is insensitive to geometric distortions as well as common image processing operations is presented.
- Graph Convolutional Network Hashing for Cross-Modal Retrieval149
A Graph Convolutional Hashing approach, which learns modality-unified binary codes via an affinity graph, and demonstrates that the proposed GCH outperforms the state-of-the-art methods.
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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