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- Heng Tao ShenSuggested from co-authorship
- Zhenmin TangSuggested from co-authorship
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Works269 from public data
- Supervised Discrete Hashing1,231
This work proposes a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification, and introduces an auxiliary variable to reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm.
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- Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval458
A novel cross- modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints, and an effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes.
- 407
- Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions352
This paper proposes an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery and forms it as a network min-cost flow problem and proposes various pruning techniques that can dramatically reduce the network size.
- Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval302
This paper introduces a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework, and is the first hashing work specifically designed for category-level SBIR with an end to end deep architecture.
- Discrete Collaborative Filtering289
This paper proposes a principled CF hashing framework called Discrete Collaborative Filtering (DCF), which directly tackles the challenging discrete optimization that should have been treated adequately in hashing, and devise a computationally efficient algorithm with a rigorous convergence proof of DCF.
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- Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation223
A graph-based global reasoning unit is introduced to strengthen the classification network’s ability to capture global relations among disjoint and distant regions and a potential object mining module is proposed to reduce the false-negative rate in pseudo labels.
- Jo-SRC: A Contrastive Approach for Combating Noisy Labels206
A noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) that trains the network in a contrastive learning manner to advance the model generalization performance by introducing consistency regularization.
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- Zero-Shot Sketch-Image Hashing175
ZSIH is the first zero- shot hashing work suitable for SBIR and cross-modal search and forms a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.
- Zero-Shot Hashing via Transferring Supervised Knowledge162
A novel hashing scheme, termed zero-shot hashing (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of " seen" categories is proposed and an efficient alternating algorithm is developed to solve the ZSH model.
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- From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis159
A new Zero-shot learning framework that can synthesise visual features for unseen classes without acquiring real images is proposed and extensive experimental results manifest that the proposed approach significantly improve the state-of-the-art results.
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- Auto-Encoding Twin-Bottleneck Hashing133
This paper proposes an efficient and adaptive code-driven graph, which is updated by decoding in the context of an auto-encoder, and introduces into the framework twin bottlenecks that exchange crucial information collaboratively.
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- Deep Asymmetric Pairwise Hashing124
This work proposes a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing, and devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly.
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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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