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- Hong-Jiang ZhangSuggested from co-authorship
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Works275 from public data
- Heterogeneous Network Embedding via Deep Architectures600
It is demonstrated that the rich content and linkage information in a heterogeneous network can be captured by a multi-resolution deep embedding function, so that similarities among cross-modal data can be measured directly in a common embedding space.
- Task Agnostic Meta-Learning for Few-Shot Learning546
An entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks, which outperforms compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.
- Stock Price Prediction via Discovering Multi-Frequency Trading Patterns524
A novel State Frequency Memory (SFM) recurrent network is proposed to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time.
- Differential Recurrent Neural Networks for Action Recognition516
This study proposes a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive frames, and thus the model is termed as differential Recurrent Neural Network (dRNN).
- Correlative multi-label video annotation504
A third paradigm is proposed which simultaneously classifies concepts and models correlations between them in a single step by using a novel Correlative Multi-Label (CML) framework and is compared with the state-of-the-art approaches in the first and second paradigms on the widely used TRECVID data set.
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- Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities366
The Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN) are presented, yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN.
- Contrastive Learning with Stronger Augmentations336
A general framework called Contrastive Learning with Stronger Augmentations (CLSA) is proposed to complement current contrastive learning approaches and the distribution divergence between the weakly and strongly augmented images over the representation bank is adopted to supervise the retrieval of strongly augmented queries from a pool of instances.
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- Image annotation by k NN-sparse graph-based label propagation over noisily tagged web images269
This article exploits the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images by proposing a novel kNN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously.
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- Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation238
This work builds upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets, and introduces a simple yet effective framework CDCL, for domain alignment.
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- Causal Attention for Vision-Language Tasks228
CATT improves various popular attention-based vision-language models by considerable margins and has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT, which uses fewer data and less computational power, comparable to the heavier UNITER.
- AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations Rather Than Data220
The experiments show that AET greatly improves over existing unsupervised approaches, setting new state-of-the-art performances being greatly closer to the upper bounds by their fully supervised counterparts on CIFAR-10, ImageNet and Places datasets.
- Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation214
This paper develops a novel deep network structure, capable of transferring labeling information across heterogeneous domains, especially from text domain to image domain, and presents a novel architecture of DTNs to translate cross-domain information from text to image.
- Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition213
A novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) is proposed to model the long-term inter-related dynamics among a group of persons for recognizing human interactions by comparing against baseline and state-of-the-art methods.
- Inferring semantic concepts from community-contributed images and noisy tags212
This paper proposes a novel sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously, and constructs an informative compact concept space with small semantic gap to infer the semantic concepts in this space to bridge the semantic gap.
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- Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection188
This work proposes a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation and achieves the object detection task by decoding the bounding box coordinates and classes.
- AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries175
The proposed Adversarial Contrastive (AdCo) model not only achieves superior performances, but also can be pre-trained more efficiently with much shorter GPU time and fewer epochs.
- CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution164
This work proposes an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone and devise a novel Local-Global Feature Cooperation Module (LGCM) to promote the consistency of local facial detail and global facial structure restoration simultaneously.
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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 2026-10-14. 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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