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Claim this profileWorks244 from public data
- Transformer Tracking1,440
This work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention and presents a Transformer tracking method based on the Siamese-like feature extraction backbone, the designed attention- based fusion mechanism, and the classification and regression head.
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- Learning Spatio-Temporal Transformer for Visual Tracking1,225
A new tracking architecture with an encoder-decoder transformer as the key component, which models the global spatio-temporal feature dependencies between target objects and search regions, while the decoder learns a query embedding to predict the spatial positions of the target objects.
- 1,147
- Deep visual tracking: Review and experimental comparison517
The background of deep visual tracking is introduced, including the fundamental concepts of visual tracking and related deep learning algorithms, and the existing deep-learning-based trackers are categorize into three classes according to network structure, network function and network training.
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- Learning Uncertain Convolutional Features for Accurate Saliency Detection480
A novel deep fully convolutional network model for accurate salient object detection and an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in the authors' decoder network are proposed.
- 462
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- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- GradNet: Gradient-Guided Network for Visual Object Tracking334
A novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations and a template generalization training method is proposed to better use gradient information and avoid overfitting.
- Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters311
This work presents a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models, and introduces a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP, respectively.
- Vision-Based Anti-UAV Detection and Tracking307
A clear and simple tracking algorithm combined with detection that inherits the detector’s high precision is proposed, thus providing a new attempt at UAV tracking using the proposed DUT Anti-UAV dataset.
- Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline298
A large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV), including 500 sequences with 1.7 million high-resolution (1920* 1080 pixels) frame pairs is constructed, and a coarse-to-fine attribute annotation is provided, where frame-level attributes are provided to exploit the potential of challenge-specific trackers.
- High-Performance Long-Term Tracking With Meta-Updater289
This work proposes a novel offline-trained Meta-Updater that can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module.
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- Structured Siamese Network for Real-Time Visual Tracking266
A local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking and can be formulated as the inference process of a conditional random field and implemented by differentiable operations, allowing the entire model to be trained in an end-to-end manner.
- LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search265
This work presents LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ and Ocean, while using much fewer model Flops and parameters.
- Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation232
This work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers’ box estimation quality and leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened Siam RPNpp) and the ARDiMP50 ( AR strengthened DiMP50) achieve good efficiency-precision trade-off.
- Learning Spatial-Aware Regressions for Visual Tracking221
This paper proposes a kernelized ridge regression model wherein the kernel value is defined as the weighted sum of similarity scores of all pairs of patches between two samples, and shows that this model can be formulated as a neural network and thus can be efficiently solved.
- Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking206
This study develops a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities and proposes a tracker switcher to switch the appearance and motion trackers flexibly.
- ‘Skimming-Perusal’ Tracking: A Framework for Real-Time and Robust Long-Term Tracking205
This work presents a novel robust and real-time long-term tracking framework based on the proposed skimming and perusal modules, designed to efficiently choose the most possible regions from a large number of sliding windows for image-wide global search.
- Correlation Tracking via Joint Discrimination and Reliability Learning189
This work proposes a novel CF-based optimization problem to jointly model the discrimination and reliability information and introduces a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker being dominated by unreliable regions.
- Learning Adaptive Attribute-Driven Representation for Real-Time RGB-T Tracking186
This work observing that the implicit attribute information can boost the model discriminability, and proposing a novel attribute-driven representation network to improve the RGB-T tracking performance, which achieves very competitive results with a real-time tracking speed.
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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 2026-10-14. 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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