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20112024
most citedLearning regression and verification networks for long-term visual tracking

73 citations · 172 across the 15 of their papers we have counts for

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14 papers · 1 filter

cs.CV2023★ 1 cited

Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning

Junwen He, Yifan Wang, Lijun Wang +6

Depth-aware panoptic segmentation is an emerging topic in computer vision which combines semantic and geometric understanding for more robust scene interpretation. Recent works pur…

cs.CV2023★ 1 cited

Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation

Yichen Yuan, Yifan Wang, Lijun Wang +5

Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identica…

cs.CV2023★ 3 cited

BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy

Zaibin Zhang, Yuanhang Zhang, Lijun Wang +2

A popular approach for constructing bird's-eye-view (BEV) representation in 3D detection is to lift 2D image features onto the viewing frustum space based on explicitly predicted d…

cs.CV2023★ 1 cited

ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data

Haojie Zhao, Junsong Chen, Lijun Wang +1

Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking. In this paper, we propose ARKitTrack, a new RGB-D tracking dataset for bo…

cs.CV2021

Towards Toxic and Narcotic Medication Detection with Rotated Object Detector

Jiao Peng, Feifan Wang, Zhongqiang Fu +4

Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry. Intelligent devices for special medication management are…

cs.CV2021

Video Annotation for Visual Tracking via Selection and Refinement

Kenan Dai, Jie Zhao, Lijun Wang +5

Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve. We pr…