Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks170 from public data
- 182
- 162
- 128
- Optimized Cartesian K-Means122
This paper proposes a novel approach, named optimized cartesian K-means (ock-mean), to better encode the data points for more accurate approximate nearest neighbor search, which can provide more flexibility and lower distortion errors than traditional methods.
- Fast Scalable Supervised Hashing92
A novel supervised hashing method, called Fast Scalable Supervised Hashing (FSSH), which circumvents the use of the large similarity matrix by introducing a pre-computed intermediate term whose size is independent with the size of training data.
- GP-BPR91
This work presents a personalized compatibility modeling scheme GP-BPR, comprising of two essential components: general compatibility modeling and personal preference modeling, which characterize the item-item and user-item interactions, respectively.
- 86
- 82
- High-order nonlocal Hashing for unsupervised cross-modal retrieval79
A new deep hashing model, termed as High-order Nonlocal Hashing (HNH) to facilitate cross-modal retrieval with the following advantages: a high-order affinity measure that considers the multi- modal neighbourhood structures from a nonlocal perspective, thereby comprehensively capturing the similarity relationships between data items.
- 78
- 78
- Label Embedding Online Hashing for Cross-Modal Retrieval77
A novel supervised online cross-modal hashing method, i.e., Label EMbedding ONline hashing, LEMON, which builds a label embedding framework including label similarity preserving and label reconstructing, which may generate discriminative binary codes and reduce the computational complexity.
- 76
- Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification74
A novel Cross-layer and Cross-sample feature optimization Network for FS-FGIC, C2-Net for short, which outperforms other state-of-the-art methods by a significant margin in most cases.
- Routing Micro-videos via A Temporal Graph-guided Recommendation System71
This work designs a novel graph-based sequential network to simultaneously model users' dynamic and diverse interest and introduces users' multi-level interest into the recommendation model via a user matrix that is able to learn the enhanced representation of users' interest.
- SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing69
A novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH), which can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information.
- Multi-modal Preference Modeling for Product Search68
This work proposes a multi-modal personalized product search method, which aims to search products which not only are relevant to the submitted textual query, but also match the user preferences from both textual and visual modalities.
- Chat More: Deepening and Widening the Chatting Topic via A Deep Model63
A novel deep scheme consisting of three channels, namely global, wide, and deep, which trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion to generate desired responses in open-domain multi-turn dialog systems.
- DELTA: A deep dual-stream network for multi-label image classification62
A novel deep Dual-stream nEtwork for the muLTi-lAbel image classification task, DELTA, which significantly outperforms several state-of-the-art methods and can automatically locate the key image patterns that trigger the labels.
- Effective neural algorithms for the traveling salesman problem62
Two new neural algorithms are presented that improve the first one by adaptively changing the neural network and thus the optimization function and the second one is a neural implementation of the Karp and Steele algorithm based on a generalized neural network.
- SCRATCH59
This paper presents a novel supervised cross-modal hashing method, SCRATCH, which leverages the collective matrix factorization on the kernelized features and the semantic embedding with labels to find a latent semantic space to preserve the intra- and inter-modality similarities.
- User Attention-guided Multimodal Dialog Systems56
Experimental results demonstrate that the proposed model outperforms the existing state-of-the-art methods by integrating the multimodal utterances and encoding the visual features based on the users' attribute-level attention.
- Supervised Hierarchical Deep Hashing for Cross-Modal Retrieval55
This paper proposes an effective cross-modal hashing method, named Supervised Hierarchical Deep Cross- modal Hashing, SHDCH for short, to learn hash codes by explicitly delving into the hierarchical labels by directly delvinginto the hash-code learning.
- 54
- Discrete Multi-view Hashing for Effective Image Retrieval54
A novel hashing method, i.e., Discrete Multi-view Hashing (DMVH), which can work on multi-view data directly and make full use of rich information in multi-View data, and a novel approach to construct similarity matrix, which can not only preserve local similarity structure, but also keep semantic similarity between data points.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.