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- H. K. HuangSuggested from co-authorship
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Works305 from public data
- 3,432
- 2,188
- Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification1,231
A quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID, which can lead to the model output with a larger inter- class variation and a smaller intra-class variation compared to the triplet loss.
- The 2005 PASCAL Visual Object Classes Challenge742
This chapter provides details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved in the PASCAL Visual Object Classes Challenge.
- TEA: Temporal Excitation and Aggregation for Action Recognition575
This paper proposes a Temporal Excitation and Aggregation block, including a motion excitation module and a multiple temporal aggregation module, specifically designed to capture both short- and long-range temporal evolution, and achieves impressive results at low FLOPs on several action recognition benchmarks.
- 558
- Local Features and Kernels for Classication of Texture and Object Categories: A Comprehensive Study531
A large-scale evaluation of an approach that represents images as distributions as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the 2 distance.
- Brief review of invariant texture analysis methods524
This paper considers invariant texture analysis, and approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed.
- Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge333
There is a cluster of four methods that rank significantly better than the other methods, with one clear winner, and the inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners.
- Video Understanding With Large Language Models: A Survey332
The emergent capabilities of Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity (abstract, temporal, and spatiotemporal) reasoning combined with common-sense knowledge, suggesting a promising path for future video understanding.
- 285
- Dataset Issues in Object Recognition261
Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets.
- Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images219
The effectiveness and generalization capability of the proposed system show its potential for real‐world clinical practice and are the highest achieved in the challenge, suggesting the proposed method is the state‐of‐the‐art.
- Not Just Privacy208
A noisy training method is proposed to enhance the cloud-side network robustness to perturbed data and can not only preserve privacy but also improve the inference performance.
- A Multi-Task Deep Network for Person Re-Identification198
This paper proposes a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID, and is the first to integrate both tasks in one network to solve the person re-identification problem.
- 180
- Discriminative Learning of Latent Features for Zero-Shot Recognition173
This work proposes an end-to-end network that is capable of automatically discovering discriminative regions by a zoom network and learning discrim inative semantic representations in an augmented space introduced for both user-defined and latent attributes.
- 152
- Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study142
This paper presents a large-scale evaluation of an approach that represents images as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover's Distance and the chi-square distance.
- Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine142
A novel and user-friendly tumor segmentation method is proposed by exploring one-class support vector machine (SVM), which has the ability of learning the nonlinear distribution of the tumor data without using any prior knowledge.
- 141
- Human action segmentation and recognition via motion and shape analysis132
An automated video analysis system which addresses segmentation and detection of human actions in an indoor environment, such as a gym, and applies a novel approach for human action recognition by describing human actions using motion and shape features.
- 125
- Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation112
A novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor achieves improved segmentation performance by incorporating boundary information directly into the loss function.
- Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography104
A method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs.
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 2026-10-16. 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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