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Works144 from public data
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- 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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- RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments412
This work addresses the issue of ground truth annotation by measuring head pose using a motion capture system and eye gaze using mobile eyetracking glasses and applies semantic image inpainting to the area covered by the glasses to bridge the gap between training and testing images by removing the obtrusiveness of the glasses.
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- Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning291
A new numerically stable form of the Laplacian sharpening by incorporating the signed graphs is proposed and a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks.
- FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation275
A fixed ratio-based mixup is introduced to augment multiple intermediate domains between the source and target domain and gradually transfer domain knowledge from the source to the target domain.
- Context-Aware Deep Feature Compression for High-Speed Visual Tracking229
A new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers and introduces extrinsic denoising processes and a new orthogonality loss term for pre-training and fine-tuning of the expert autoencoders.
- FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism221
A fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation and a novel decoupled rotation mechanism that employs two decoders to complementarily access the rotation information.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- G2L-Net: Global to Local Network for Real-Time 6D Pose Estimation With Embedding Vector Features121
A novel real-time 6D object pose estimation framework, named G2L-Net, which operates on point clouds from RGB-D detection in a divide-and-conquer fashion and achieves state-of-the-art performance in terms of both accuracy and speed.
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- HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation94
A simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks and outperforms the state-of-the-art methods by a large margin.
- Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution91
This work proposes a collaborative learning strategy where two-branches of deep networks are learning from each other, and achieves beyond state-of-the-art accuracy in 3D pose estimation, as well as recovers dense 3D hand and object shapes.
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- DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self77
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both human and robot.
- PMnet: learning of disentangled pose and movement for unsupervised motion retargeting72
The proposed deep learning framework for unsupervised motion retargeting outperforms the state-of-the-art (sota) method by reducing the motion retargeting error from 7.68 (sota) to 1.95 (the authors').
- Robust action recognition using local motion and group sparsity67
This paper robustly identify local motions of interest in an unsupervised manner by taking advantage of group sparsity to robustly classify action types and demonstrates that the group sparse representation with the multiple kernel method can dramatically improve the action recognition performance.
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- SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation62
This paper proposes a novel method that generates a synthetic dataset that mimics natural human hand movements by re-engineering annotations of an extant static hand pose dataset into pose-flows and shows that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand poses estimation benchmarks.
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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