Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks76 from public data
- Implicit Sample Extension for Unsupervised Person Re-Identification144
An Implicit Sample Extension method to generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy, which reduces the “sub and mixed” clustering errors, thus improving the Re-ID performance.
- 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.
- 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.
- 84
- 73
- 64
- 49
- Robust visual tracking with channel attention and focal loss35
This work introduces channel attention and focal loss into the network design to enhance feature representation learning and demonstrates that the enhanced tracker achieves significant performance improvement while running at a real-time frame-rate of 66 fps.
- 34
- Cross-ratio invariant based line scan camera geometric calibration with static linear data33
A flexible new technique to calibrate the geometric model of a line scan camera by using the properties of cross-ratio and point correspondences, which is easy to use and flexible.
- 26
- 23
- 22
- Drone-Based Visible–Thermal Object Detection with Transformers and Prompt Tuning18
This study introduces an innovative dual-modal object detection framework called Visual Prompt multi-modal Detection (VIP-Det) that harnesses the Transformer architecture as the primary feature extractor and integrates vision prompts for refined feature fusion.
- Masked and dynamic Siamese network for robust visual tracking18
Two modules are proposed, namely the target objectness model and the target template model, based on existing Siamese network based trackers to solve the distraction problem and the Gaussian mixed model enhances diversity and simultaneously reduces redundancy between target samples.
- Double HEVC Compression Detection with the Same QPs Based on the PU Numbers18
A novel method based on the same Qps is applied in dealing with double HEVC compression detection and it is proved that the proposed algorithm is of high classification accuracy for detecting doubleHEVC compression.
- 17
- Learning target-aware correlation filters for visual tracking17
The TACF formulation is proposed, which proposes an optimization strategy based on the Preconditioned Conjugate Gradient method for efficient filter learning and achieves state-of-the-art performance on OTB100 and the top rank in EAO on the VOT2016 challenge.
- Learning adaptively windowed correlation filters for robust tracking17
A generic DCF tracking framework which suppresses background information and highlights the foreground object with an object likelihood map computed from the color histograms is proposed which can be combined with many DCF trackers and achieves significant performance improvement.
- An Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing15
An intelligent method to calibrate radar and camera sensors for data fusing without human assistance operation in the general scenarios with low time cost or easy operation is introduced.
- 15
- 15
- Drone Based RGBT Tracking with Dual-Feature Aggregation Network13
A Dual-Feature Aggregation Network that utilizes attention mechanisms in both spatial and channel dimensions to aggregate heterogeneous modality feature information to better address the problem of visible-infrared information fusion is proposed.
- 13
- Self-Guided Hard Negative Generation for Unsupervised Person Re-Identification13
A joint framework is developed which incorporates a hard negative generation network (HNGN) and a re-ID network which is alternately trained in an adversarial manner to improve each other, where the reID network guides H NGN to generate challenging data and HNGN enforces the re-IDs to enhance discrimination ability.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.