Search Results for author: Guorong Li

Found 18 papers, 11 papers with code

Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations

no code implementations ECCV 2020 Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, Nicu Sebe

We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers.

Crowd Counting

Towards Unified Token Learning for Vision-Language Tracking

1 code implementation27 Aug 2023 Yaozong Zheng, Bineng Zhong, Qihua Liang, Guorong Li, Rongrong Ji, Xianxian Li

In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a token generation task.

Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes

1 code implementation ICCV 2023 Di wu, Pengfei Chen, Xuehui Yu, Guorong Li, Zhenjun Han, Jianbin Jiao

Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects).

Multiple Instance Learning object-detection +1

Consistency-Aware Anchor Pyramid Network for Crowd Localization

no code implementations8 Dec 2022 Xinyan Liu, Guorong Li, Yuankai Qi, Zhenjun Han, Qingming Huang, Ming-Hsuan Yang, Nicu Sebe

Crowd localization aims to predict the spatial position of humans in a crowd scenario.

Progressive Multi-resolution Loss for Crowd Counting

no code implementations8 Dec 2022 Ziheng Yan, Yuankai Qi, Guorong Li, Xinyan Liu, Weigang Zhang, Qingming Huang, Ming-Hsuan Yang

Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth.

Crowd Counting

A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game

1 code implementation13 Sep 2022 Ke Ma, Qianqian Xu, Jinshan Zeng, Guorong Li, Xiaochun Cao, Qingming Huang

From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim.

Information Retrieval Retrieval

Object Localization under Single Coarse Point Supervision

2 code implementations CVPR 2022 Xuehui Yu, Pengfei Chen, Di wu, Najmul Hassan, Guorong Li, Junchi Yan, Humphrey Shi, Qixiang Ye, Zhenjun Han

In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.

Multiple Instance Learning Object Localization

Hierarchical Modular Network for Video Captioning

1 code implementation CVPR 2022 Hanhua Ye, Guorong Li, Yuankai Qi, Shuhui Wang, Qingming Huang, Ming-Hsuan Yang

(II) Predicate level, which learns the actions conditioned on highlighted objects and is supervised by the predicate in captions.

Representation Learning Video Captioning

Rethinking Sampling Strategies for Unsupervised Person Re-identification

2 code implementations7 Jul 2021 Xumeng Han, Xuehui Yu, Guorong Li, Jian Zhao, Gang Pan, Qixiang Ye, Jianbin Jiao, Zhenjun Han

While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.

Pseudo Label Representation Learning +1

Learning to Filter: Siamese Relation Network for Robust Tracking

1 code implementation CVPR 2021 Siyuan Cheng, Bineng Zhong, Guorong Li, Xin Liu, Zhenjun Tang, Xianxian Li, Jing Wang

RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result.

Meta-Learning Relation Network

Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking

1 code implementation21 Jan 2021 Nan Jiang, Kuiran Wang, Xiaoke Peng, Xuehui Yu, Qiang Wang, Junliang Xing, Guorong Li, Jian Zhao, Guodong Guo, Zhenjun Han

The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.

Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network

no code implementations ICCV 2021 Xinyan Liu, Guorong Li, Zhenjun Han, Weigang Zhang, Yifan Yang, Qingming Huang, Nicu Sebe

Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.

Crowd Counting

Siamese Box Adaptive Network for Visual Tracking

2 code implementations CVPR 2020 Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, Rongrong Ji

Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target.

Visual Tracking

Real-time Visual Object Tracking with Natural Language Description

no code implementations26 Jul 2019 Qi Feng, Vitaly Ablavsky, Qinxun Bai, Guorong Li, Stan Sclaroff

In benchmarks, our method is competitive with state of the art trackers, while it outperforms all other trackers on targets with unambiguous and precise language annotations.

Visual Object Tracking

Spatiotemporal CNN for Video Object Segmentation

1 code implementation CVPR 2019 Kai Xu, Longyin Wen, Guorong Li, Liefeng Bo, Qingming Huang

Specifically, the temporal coherence branch pretrained in an adversarial fashion from unlabeled video data, is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation.

Semantic Segmentation Semi-Supervised Video Object Segmentation +3

The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking

no code implementations ECCV 2018 Dawei Du, Yuankai Qi, Hongyang Yu, Yifan Yang, Kaiwen Duan, Guorong Li, Weigang Zhang, Qingming Huang, Qi Tian

Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.

Multiple Object Tracking object-detection +2

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