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- Peng Un MakSuggested from co-authorship
- Michael Rung-Tsong LyuSuggested from co-authorship
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Works165 from public data
- A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration1,469
This paper presents a very appealing tracker based on the correlation filter framework and suggests an effective scale adaptive scheme to tackle the problem of the fixed template size in kernel correlation filter tracker.
- Deep Learning for Content-Based Image Retrieval941
This paper investigates a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIr tasks under varied settings.
- Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions674
A differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural network (CNN-PP).
- Oriented RepPoints for Aerial Object Detection552
This paper proposes an effective adaptive points learning approach to aerial object detection by taking advantage of the adaptive points representation, which is able to capture the geometric information of the arbitrary-oriented instances.
- Batch mode active learning and its application to medical image classification524
A framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously and is more effective than the state-of-the-art algorithms for active learning.
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- Osprey: Pixel Understanding with Visual Instruction Tuning212
Osprey is proposed, a mask-text instruction tuning approach, to extend MLLMs by incor-porating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding.
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- Semisupervised SVM batch mode active learning with applications to image retrieval182
This work proposes a novel scheme for explicitly addressing the drawbacks of conventional SVM active learning, which first learns a kernel function from a mixture of labeled and unlabeled data, and therefore alleviates the problem of small-sized training data.
- AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance166
This paper proposes to create an AI coach system to provide personalized athletic training experiences with several distinct features, and shows that the proposed system can lead to a remarkably better user training experience by extensive user studies.
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- SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation With Surgical Robotics128
This letter proposes a novel surgical perception framework, SuPer, for surgical robotic control that successfully completed soft tissue manipulation tasks with high accuracy and was implemented on the da Vinci Surgical System in real-time with an end-effector controller.
- Feature agglomeration networks for single stage face detection128
A novel framework of "Feature Agglomeration Networks" (FAN) is proposed to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently.
- A survey of human pose estimation: The body parts parsing based methods126
A general framework of human pose estimation is presented, and the latest technical progress on each stage is discussed, to discuss the limitations of the existing approaches and foresee the future directions to be explored.
- Near-duplicate keyframe retrieval by nonrigid image matching112
A novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora and promising results show that the proposed method is more effective than other state-of-the-art approaches for near- DUplicateKeyframe retrieval.
- Robust Estimation of Similarity Transformation for Visual Object Tracking105
This paper proposes a novel correlation filter-based tracker with robust estimation of similarity transformation on the large displacements to tackle this challenging problem of efficiently search in such a large 4-DoF space in real-time.
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- DeepFacade: A Deep Learning Approach to Facade Parsing100
This paper is the first to employ end-to-end deep convolutional neural network on full image scale in the task of building facades parsing, and pro-poses a symmetric regularizer for training the neural network.
- Distance metric learning from uncertain side information with application to automated photo tagging97
A novel probabilistic distance metric learning scheme is proposed that automatically derives constraints from the uncertain side information, and efficiently learns a distance metric from the derived constraints.
- A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation91
This work proposes a novel spatial-temporal QoS prediction approach for time-aware Web service recommendation, where a sparse representation is employed to model QoS variations, and uses the geo-location of web service to effectively select the nearest neighbor for the sparse representation of temporal QoS values.
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- Improving Nighttime Driving-Scene Segmentation via Dual Image-Adaptive Learnable Filters88
DAIL-Filters can significantly improve the supervised segmentation performance on ACDC_Night and NightCity datasets, while it demonstrates the state-of-the-art performance on unsupervised nighttime semantic segmentation on Dark Zurich and Nighttime Driving testbeds.
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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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