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Works97 from public data
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
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- Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification422
An improved two-stream CNN network is presented to learn the multi-modality sharable feature representations and identity loss and contrastive loss are integrated to enhance the discriminability and modality-invariance with partially shared layer parameters.
- Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking413
A dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations and identity loss is further incorporated to model the identity-specific information to handle large intra-class variations.
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- Regularized Fine-Grained Meta Face Anti-Spoofing195
This work casts face anti-spoofing as a domain generalization (DG) problem, and attempts to address this problem by developing a new meta-learning framework called Regularized Fine-grained Meta-learning, which incorporates the domain knowledge of faceAnti-sp spoofing as the regularization so that meta- learning is conducted in the feature space regularized by the supervision of domain knowledge.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System123
A new tracking system, which aims to combine the information from RGB and infrared modalities for object tracking, based on the proposed machine learning model that can alleviate the modality discrepancy issue under the proposed modality consistency constraint.
- Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection123
A new liveness feature, called rPPG correspondence feature (CFrPPG) is proposed to precisely identify the heartbeat vestige from the observed noisy rP PG signals to overcome the global interferences.
- Modality-correlation-aware sparse representation for RGB-infrared object tracking119
This paper presents a feature representation and fusion model to combine the feature representation of the object in RGB and infrared modalities for object tracking and demonstrates the effectiveness of the proposed method.
- CricaVPR: Cross-Image Correlation-Aware Representation Learning for Visual Place Recognition115
A robust global representation method with cross-image correlation awareness for VPR, named CricaVPR, which uses the attention mechanism to correlate multiple images within a batch and introduces the multi-scale local information to further enhance the cross-image correlation-aware representation.
- Modality-aware Collaborative Learning for Visible Thermal Person Re-Identification114
A novel modality-aware collaborative (MAC) learning method on top of a two-stream network for VT-ReID, which handles the modalities-discrepancy in both feature level and classifier level, and introduces a collaborative learning scheme, which regularizes themodality-sharable and modality -specific identity classifiers by utilizing the relationship between different classifiers.
- Robust Anchor Embedding for Unsupervised Video Person re-IDentification in the Wild111
This paper addresses the scalability and robustness issues of estimating labels from imbalanced unlabeled data for unsupervised video-based person re-identification (re-ID) and proposes a novel Robust AnChor Embedding (RACE) framework via deep feature representation learning for large-scale un Supervised video re-ID.
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- Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition100
A hybrid adaptation method to achieve both global and local adaptation efficiently, in which only lightweight adapters are tuned without adjusting the pre-trained model, and a mutual nearest neighbor local feature loss, which ensures proper dense local features are produced for local matching and avoids time-consuming spatial verification in re-ranking.
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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 2026-10-15. 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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