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- Person Re-Identification by Support Vector Ranking772
This work converts the person re-identification problem from an absolute scoring p roblem to a relative ranking problem and develops an novel Ensemble RankSVM to overcome the scalability limitation problem suffered by existing SVM-based ranking methods.
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- Associating Groups of People510
A novel people group representation and a group matching algorithm are proposed that addresses changes in the relative positions of people in a group and the latter deals with variations in illumination and viewpoint across camera views.
- Unsupervised Person Re-Identification by Soft Multilabel Learning421
The reference agent learning to represent each reference person by a reference agent in a joint embedding is introduced and the unified deep model outperforms the state-of-the-art unsupervised RE-ID methods by clear margins.
- MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection398
A multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations with only video-level annotations, which performs comparably to or even better than existing supervised and weakly supervised methods.
- DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition396
This work analyzes the patch interaction of global attention in ViTs and proposes Multi-Scale Dilated Attention (MSDA) to model local and sparse patch interaction within the sliding window, indicating the redundancy of global dependency modeling in shallow layers of ViTs.
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- Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification342
The model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images, and finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved.
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- Person Re-Identification by Camera Correlation Aware Feature Augmentation338
This work forms a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coR relation Aware Feature augmenTation (CRAFT), and presents a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT.
- Squeeze-and-Attention Networks for Semantic Segmentation335
A novel squeeze-and-attention network (SANet) architecture is proposed that leverages an effective squeeze- and-att attention (SA) module to account for two distinctive characteristics of segmentation: i) pixel-group attention, and ii) pixels-wise prediction.
- Embedding Deep Metric for Person Re-identification: A Study Against Large Variations313
A novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation is proposed and the learning by a metric weight constraint is improved, so that the learned metric has a better generalization ability.
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- VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness299
VBench-2.0 aims to set a new standard for the next generation of video generative models in pursuit of intrinsic faithfulness, a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness.
- Person Re-Identification by Contour Sketch Under Moderate Clothing Change293
This work performs cross-clothes person re-id based on a contour sketch of person image to take advantage of the shape of the human body instead of color information for extracting features that are robust to moderate clothing change, and develops a multistream network for aggregating multi-granularity features to better identify a person.
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- Top-Push Video-Based Person Re-identification261
A top-push distance learning model (TDL), in which a top- push constrain enforces the optimization on top-rank matching in re-id, is proposed to make the matching model more effective towards selecting more discriminative features to distinguish different persons.
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- 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.
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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