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- Detection and Tracking Meet Drones Challenge1,344
This work provides a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and concludes the challenge as well as propose future directions.
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- The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking1,076
This work constructs a new UAV benchmark focusing on complex scenarios with new level challenges, and is the first time to explore issues in unconstrained scenes comprehensively in UAV based real scenes.
- UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking799
This work performs a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
- 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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- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results259
Based on the selected 29 robust detection methods, the experimental results comprehensively are discussed, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection.
- The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline247
A novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT is proposed.
- Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization242
An attention convolutional binary neural tree architecture is presented to address problems for weakly supervised Fine-grained visual categorization and uses the attention transformer module to enforce the network to capture discriminative features.
- Vision Meets Drones: Past, Present and Future173
The VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South, is described, being the largest such dataset ever published, and enables extensive evaluation and investigation of visual analysis algorithms on the drone platform.
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- Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark139
The Space-Time Neighbor-Aware Network (STNNet) is designed as a strong baseline to solve object detection, tracking and counting jointly in dense crowds and performs favorably against the state-of-the-arts.
- Spatial Attention Pyramid Network for Unsupervised Domain Adaptation137
A new spatial attention pyramid network that combines the dense global structure representation and local texture patterns at each spatial location effectively using the spatial attention mechanism to focus on the discriminative regions with context information for domain adaption.
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- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
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- Multi-Granularity Alignment Domain Adaptation for Object Detection114
A unified multi-granularity alignment based object detection framework towards domain-invariant feature learning that leverages not only the instance discriminability in different categories but also the category consistency between two domains.
- Deep low-rank subspace ensemble for multi-view clustering106
A new multi-view clustering method that integrates deep matrix factorization, low-rank subspace learning, and multiple subspace ensemble in a unified framework, which is term as the Deep Low-Rank Subspace Ensemble (DLRSE) method is proposed.
- Dual Encoding for Abstractive Text Summarization94
This paper model abstractive text summarization using a dual encoding model that employs a dual encoder including the primary and the secondary encoders to generate more diverse summary that can decrease repetition phenomenon for long sequence generation.
- Cascade Transformers for End-to-End Person Search91
The Cascade Occluded Attention Transformer (COAT) for end-to-end person search uses a three-stage cascade design that focuses on detecting people in the first stage, while later stages simultaneously and progressively refine the representation for person detection and re-identification.
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- VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results87
The evaluation protocol of the VisDrone-SOT2018 challenge is presented and the results of a comparison of 22 trackers on the benchmark dataset are presented, which are publicly available on the challenge website.
- Multi-Drone-Based Single Object Tracking With Agent Sharing Network83
An agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones, which can improve the tracking accuracy significantly compared with single drone tracking.
- VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results81
A large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames, and the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset are presented.
- Guided Attention Network for Object Detection and Counting on Drones77
This paper proposes a new Guided Attention network (GAnet) to deal with both object detection and counting tasks based on the feature pyramid, and fuse different scales of feature maps by using the proposed weakly-supervised Background Attention between the background and objects for more semantic feature representation.
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- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- DETRAC: A New Benchmark and Protocol for Multi-Object Tracking63
This work performs a comprehensive quantitative study on the effect of object detection accuracy to the overall MOT performance based on a new large-scale DETection and tRACking (DETRAC) benchmark dataset.
- Learning Semantic Neural Tree for Human Parsing61
A novel semantic neural tree for human parsing is designed, which uses a tree architecture to encode physiological structure of human body, and designs a coarse to fine process in a cascade manner to generate accurate results.
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- Geometric Hypergraph Learning for Visual Tracking58
A geometric hypergraph learning-based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in different frames, and a confidence-aware sampling method is developed to select representative vertices and hyperedges to construct the geometric hyper graph for more robustness and scalability.
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- Open Set Action Recognition via Multi-Label Evidential Learning48
This work proposes a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel action detection methods by addressing the more general problems of single or multiple actors in the same scene, with simultaneous action(s) by any actor.
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- Learning Non-Uniform Hypergraph for Multi-Object Tracking41
A new near-online MOT algorithm based on non-uniform hypergraph, which can model different degrees of dependencies among tracklets in a unified objective and achieves favorable performance against the state-of-the-art MOT methods.
- MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification37
A semi-automatic annotation framework and GUI that combines state-of-the-art real-time models for object detection, pose estimation, person ReID, and multi-object tracking is developed and evaluated.
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- Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification35
The proposed Deep Correlated Predictive Subspace Learning (DCPSL) method is able to learn proper subspace representation that is suitable for class label prediction, which can further improve the performance of classification.
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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 2026-10-13. 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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