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- Stan Ziqing LiSuggested from co-authorship
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Works105 from public data
- Single-Shot Refinement Neural Network for Object Detection1,627
This paper proposes a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one- stage methods.
- 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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- 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.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
- Occlusion-Aware R-CNN: Detecting Pedestrians in a Crowd467
A new occlusion-aware R-CNN (OR-CNN) is proposed to improve the detection accuracy in the crowd and a new aggregation loss is designed to enforce proposals to be close and locate compactly to the corresponding objects.
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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.
- 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.
- Stochastic online AUC maximization223
It is shown that AUC optimization can be equivalently formulated as a convex-concave saddle point problem and a stochastic online algorithm (SOLAM) is proposed which has time and space complexity of one datum.
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- 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.
- DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training157
This work introduces a lightweight visual-aware language decoder that only requires the text data for training, easing the burden on the collection of paired data, and proposes a training-free mechanism to reduce the modality gap.
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- ScratchDet: Training Single-Shot Object Detectors From Scratch142
The ScratchDet achieves the state-of-the-art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the train-from-scratch detectors and even performs better than several one-stage pretrained methods.
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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.
- 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.
- Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark130
This work collects a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, and designs the selective dense attention network (SDANet), which consists of the dense attention module and the dependency refinement module.
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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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