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- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- Prompting for Multi-Modal Tracking215
A novel multi-modal prompt tracker (ProTrack), which can transfer the multi- modal inputs to a single modality by the prompt paradigm, and can achieve high-performance multi- Modal tracking by only altering the inputs, even without any extra training on multi-Modal data.
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- DepthTrack: Unveiling the Power of RGBD Tracking130
A new RGBD tracking dataset - Depth-Track - is introduced that has twice as many sequences and scene types than in the largest existing dataset, and three times more objects, and the average length of the sequences, number of deformable objects and the number of annotated tracking attributes have been increased.
- 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.
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- 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.
- Depth-aided Camouflaged Object Detection60
Inspired by research on biology and evolution, depth information is introduced as an additional cue to help break camouflage, which can provide spatial information and texture-free separation for foreground and background.
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- KD-SCFNet: Towards more accurate and lightweight salient object detection via knowledge distillation27
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- X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs19
X-Former is a lightweight transformer module designed to exploit the complementary strengths of CL and MIM through an innovative interaction mechanism that bootstraps vision-language representation learning and multimodal-to-multimodal generative learning from two frozen vision encoders.
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- Effect of the Kickstart exoskeleton lower extremity walking system on improving lower extremity walking ability in subacute stroke patients: a randomized controlled trial13
The Kickstart® Walk assist system can increase stroke patients’ lower limb strength and motor function and improve their walking ability and there is a lack of high-quality, randomized controlled trials to validate its effectiveness.
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- Towards Generic 3D Tracking in RGBD Videos: Benchmark and Baseline7
This paper investigates a novel problem: is it possible to track a generic 3D object in RGBD videos and predict 3D bounding boxes of the object of interest and proposes an effective tracking baseline to estimate 3D bounding boxes for arbitrary objects in RGBD videos, by fusing appearance and spatial information effectively.
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- Depth-only Object Tracking5
The end-to-end trained RGBD-DiMP outperforms the recent VOT 2020 RGBD winners and generates depth maps for tracking from the scratch with the generated data and fine-tune it with the available small RGBD tracking datasets.
- Infrared target tracking based on template adaptive Mean Shift4
An algorithm combining improved Mean Shift with incremental Support Vector Machine for infrared target tracking with machine learning theory introduced and incremental support vector machine was used to update target template adaptively, thus target tracking problem was converted to a problem of classification between the target and the background.
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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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