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- Hanqing LuSuggested from co-authorship
- Jinhui TangSuggested from co-authorship
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Works264 from public data
- Unsupervised Feature Selection Using Nonnegative Spectral Analysis545
A new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), which exploits the discriminative information and feature correlation simultaneously to select a better feature subset.
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- Learning Dual Convolutional Neural Networks for Low-Level Vision221
The proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner, and can generate the target signals according to the formation model for each particular application.
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- Weakly-supervised Semantic Guided Hashing for Social Image Retrieval192
This work proposes a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data structures.
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- BlockMix: Meta Regularization and Self-Calibrated Inference for Metric-Based Meta-Learning166
This work proposes new mechanisms to learn generalized and discriminative feature embeddings as well as improve the robustness of classifiers against prediction corruptions for meta-learning by integrating interpolation on the images and labels within metric learning.
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- Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains144
A novel generalized deep transfer networks (DTNs) capable of transferring label information across heterogeneous domains, textual domain to visual domain, and to share the labels between two domains are proposed, able to generate domain-specific and shared interdomain features.
- Participation-Contributed Temporal Dynamic Model for Group Activity Recognition140
A novel Participation-Contributed Temporal Dynamic Model (PC-TDM) to recognize group activity, which mainly consists of a "One" network and a " one to Key" network, which aims at modeling the individual dynamic of each person.
- M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition134
M3Net is proposed, a matching-based framework for FS-FG action recognition, which incorporates multi-view encoding, multi- view matching, and multi-View fusion to facilitate embedding encoding, similarity matching,and decision making across multiple viewpoints.
- VRP-SAM: SAM with Visual Reference Prompt131
A novel Visual Reference Prompt encoder that empowers the Segment Any-thing Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model, which achieves a breakthrough within the SAM framework by extending its versatility and applicabil-ity while preserving SAM's inherent strengths, thus enhancing user-friendliness.
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- Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification123
A novel hybrid deep learning framework is introduced that integrates useful clues from multiple modalities, including static spatial appearance information, motion patterns within a short time window, audio information, as well as long-range temporal dynamics.
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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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