Search Results for author: Yuanyou Xu

Found 7 papers, 4 papers with code

Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation

1 code implementation ICCV 2023 Yuanyou Xu, Zongxin Yang, Yi Yang

Tracking any given object(s) spatially and temporally is a common purpose in Visual Object Tracking (VOT) and Video Object Segmentation (VOS).

Object Representation Learning +6

ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation

no code implementations5 Jul 2023 Jiahao Li, Yuanyou Xu, Zongxin Yang, Yi Yang, Yueting Zhuang

The Associating Objects with Transformers (AOT) framework has exhibited exceptional performance in a wide range of complex scenarios for video object segmentation.

Object Position +4

ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: TREK-150 Single Object Tracking

no code implementations5 Jul 2023 Yuanyou Xu, Jiahao Li, Zongxin Yang, Yi Yang, Yueting Zhuang

MSDeAOT efficiently propagates object masks from previous frames to the current frame using two feature scales of 16 and 8.

Object Segmentation +4

Segment and Track Anything

1 code implementation11 May 2023 Yangming Cheng, Liulei Li, Yuanyou Xu, Xiaodi Li, Zongxin Yang, Wenguan Wang, Yi Yang

This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video.

Autonomous Driving Object Tracking

Video Object Segmentation in Panoptic Wild Scenes

2 code implementations8 May 2023 Yuanyou Xu, Zongxin Yang, Yi Yang

Considering the challenges in panoptic VOS, we propose a strong baseline method named panoptic object association with transformers (PAOT), which uses panoptic identification to associate objects with a pyramid architecture on multiple scales.

Object Semantic Segmentation +2

Semantic Segmentation of Panoramic Images Using a Synthetic Dataset

1 code implementation2 Sep 2019 Yuanyou Xu, Kaiwei Wang, Kailun Yang, Dongming Sun, Jia Fu

In addition, it has been shown that by using panoramic images with a 180 degree FoV as training data the model has better performance.

Segmentation Semantic Segmentation

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