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Works72 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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- 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.
- Partially View-aligned Clustering151
A novel multi-view clustering method termed partially view-aligned clustering (PVC), which proposes to use a differentiable surrogate of the non-differentiable Hungarian algorithm and recasts it as a pluggable module to establish the category-level correspondence of the unaligned data.
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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 Retrieval134
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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- MaIR: A Locality-and Continuity-Preserving Mamba for Image Restoration114
A novel Mamba-based Image Restoration model (MaIR), which consists of Nested S-shaped Scanning strategy (NSS) and Sequence Shuffle Attention block (SSA), which preserves locality and continuity of the input images through the stripe-based scanning region and the S-shaped scanning path, respectively.
- Multi-View Linear Discriminant Analysis Network106
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.
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- OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization84
This work proposes a novel One-shot Pruning-Quantization (OPQ), the first work that reveals pre-trained model is sufficient for solving pruning and quantization simultaneously, without any complex iterative/manual optimization at the finetuning stage, and proposes a unified channel-wise quantization method that leads to low bit-rate allocation without introducing extra overhead.
- Multimodal adversarial network for cross-modal retrieval74
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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- DRSL: Deep Relational Similarity Learning for Cross-modal Retrieval69
Deep Relational Similarity Learning (DRSL) is a deep hybrid framework that integrates the relation networks module for relation learning, capturing the implicit nonlinear distance metric.
- Semantic Invariant Multi-View Clustering With Fully Incomplete Information66
This work discovers the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples.
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- Dual Self-Paced Cross-Modal Hashing45
This work proposes a novel Dual Self-Paced Cross-Modal Hashing (DSCMH) that mimics human cognitive learning to learn hashing from ``easy'' to ``hard'' in both instance and feature levels, thereby embracing robustness against noise/outliers.
Publication data from OpenAlex; 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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