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- Yong XiangSuggested from co-authorship
- Yi ZhangSuggested from co-authorship
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Works224 from public data
- Contrastive Clustering862
An online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning and jointly learns representations and cluster assignments in an end-to-end manner is proposed.
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- Massive MIMO Linear Precoding: A Survey253
It is shown that a viable precoding technique for massive MIMO systems is still unknown to date, and several research potentials that are worthy of future research efforts are suggested.
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- Spectrogram based multi-task audio classification178
This paper proposes a Deep Neural Network (DNN)-based multi-task model that exploits such relationships and deals with multiple audio classification tasks simultaneously and achieves higher accuracy than task-specific models which train the models separately.
- Twin Contrastive Learning for Online Clustering173
It is found that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
- Noisy-Correspondence Learning for Text-to-Image Person Re-Identification156
A novel Robust Dual Embedding method (RDE) that can learn robust visual-semantic associations even with noisy correspondence and achieves state-of-the-art results both with and without synthetic noisy correspondences on all three datasets.
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- Deep Evidential Learning with Noisy Correspondence for Cross-modal Retrieval136
A generalized Deep Evidential Cross-modal Learning framework (DECL), which integrates a novel Cross- modal Evidential Learning paradigm (CEL) and a Robust Dynamic Hinge loss (RDH) with positive and negative learning to address the amplification problem and embrace higher robustness against high noise.
- Scalable Deep Multimodal Learning for Cross-Modal Retrieval133
Comprehensive experimental results on four widely-used benchmark datasets demonstrate that the proposed SDML is effective and efficient in multimodal learning and outperforms the state-of-the-art methods in cross-modal retrieval.
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- Deep drug-target binding affinity prediction with multiple attention blocks117
Experimental results of DTI prediction on two benchmark datasets show the proposed end-to-end model outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block.
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- Multi-View Linear Discriminant Analysis Network104
A Multi-view Linear Discriminant Analysis Network (MvLDAN) is proposed by seeking a nonlinear discriminant and view-invariant representation shared among multiple views by employing multiple feedforward neural networks and a novel eigenvalue-based multi-view objective function.
- Underdetermined Blind Source Separation Using Sparse Coding99
This paper proposes an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency representation vectors of observed mixture signals that are associated with TF points where only single source possesses dominant energy.
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- Spread Spectrum Audio Watermarking Using Multiple Orthogonal PN Sequences and Variable Embedding Strengths and Polarities91
This paper proposes a novel SS-based audio watermarking method that can embed a large number of watermark bits into an audio signal without compromising the robustness against common attacks and is computationally efficient.
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- Multimodal adversarial network for cross-modal retrieval73
A Multimodal Adversarial Network (MAN) method to project the multimodal data into a common space wherein the similarities between different modalities can be directly computed by the same distance measurement, and could be one of the first works to specifically design for the multi-modality representation learning with adversarial learning.
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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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