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Works232 from public data
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- Person re-identification with content and context re-ranking90
A novel and efficient re-ranking technque to solve the person re-identification problem in the surveillance application and can be divided into offline and online parts, where the majority of computation load is accomplished by the offline part and the online computation complexity is only proportional to the size of the gallery data set.
- One More Check: Making “Fake Background” Be Tracked Again87
This paper constructs a new one-shot tracker and reaches a new state-of-the-art MOTA and IDF1 performance and proposes a re-check network, which innovatively expands the role of ID embedding from data association to motion forecasting by effectively propagating previous tracklets to the current frame with a small overhead.
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- Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition65
An image classification framework by leveraging the non-negative sparse coding, correlation constrained low rank and sparse matrix decomposition technique (CCLR-Sc+SPM), which achieves or outperforms the state-of-the-art results on several benchmarks.
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- Equidistance constrained metric learning for person re-identification61
An algorithm for learning a Mahalanobis distance for person re-identification that outperforms the state-of-the-art methods on CUHK01, CUHK03, Market1501 and DukeMTMC-reID datasets, and achieves very competitive performance on the widely used VIPeR dataset.
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- Ranking Optimization for Person Re-identification via Similarity and Dissimilarity56
This paper proposes to use both similarity and dissimilarity cues in a ranking optimization framework for person re-identification, based on the phenomenon that the true match should not only be similar to theStrong similar samples of the probe but also dissimilar to the strong dissimilar samples.
- Image classification using spatial pyramid robust sparse coding47
A new image classification method by spatial pyramid robust sparse coding (SP-RSC), which tries to find the maximum likelihood estimation solution by alternatively optimizing over the codebook and local feature coding parameters, hence is more robust to outliers than traditional sparse coding based methods.
- Intra-View and Inter-View Supervised Correlation Analysis for Multi-View Feature Learning46
This paper proposes a novel multi-view feature learning approach based on intra-view and inter-view supervised correlation analysis (I2SCA), which can explore the useful correlation information of samples within each view and between all views.
- Coupled-View Based Ranking Optimization for Person Re-identification45
A coupled-view based ranking optimization method through cross KNN rank aggregation and graph-based re-ranking to revise the original ranking lists based on the core assumption that the images of the same person should share the similar visual appearance in both global and local views.
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- Specific Person Retrieval via Incomplete Text Description36
A specific attribute completion is conducted to enrich the original text query and generate a more expressive attribute vector and a pairwise-based metric learning is introduced for completed attribute vectors.
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- Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis35
The results indicate that the GA variable selection technique can significantly improve the accuracy rate of soil type recognition using NIR spectroscopy, suggesting that the proposed methodology is a promising alternative for recognizing soil types using Nir spectra.
- Image classification using Harr-like transformation of local features with coding residuals35
This paper proposes a novel image classification method using Harr-like transformation of local features with additional consideration of coding residuals to reduce the information loss during the local feature encoding process.
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 2026-10-15. 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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