1 code implementation • ECCV 2020 • Xingping Dong, Jianbing Shen, Ling Shao, Fatih Porikli
To make full use of these sequence-specific samples, {we propose a compact latent network to quickly adjust the tracking model to adapt to new scenes.}
no code implementations • 6 Jun 2023 • Yukun Zhai, Xiaoqiang Zhang, Xiameng Qin, Sanyuan Zhao, Xingping Dong, Jianbing Shen
End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework.
1 code implementation • CVPR 2023 • Dongming Wu, Wencheng Han, Tiancai Wang, Xingping Dong, Xiangyu Zhang, Jianbing Shen
In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT).
no code implementations • 8 Feb 2023 • Jiawei Liu, Xingping Dong, Sanyuan Zhao, Jianbing Shen
To achieve simultaneous detection for both common and rare objects, we propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes.
no code implementations • 2 Feb 2023 • Xingping Dong, Jianbing Shen, Fatih Porikli, Jiebo Luo, Ling Shao
Under this viewing, we perform an in-depth analysis for them through visual simulations and real tracking examples, and find that the failure cases in some challenging situations can be regarded as the issue of missing decisive samples in offline training.
1 code implementation • 27 Sep 2022 • Xingping Dong, Jianbing Shen, Ling Shao
In this work, we prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
no code implementations • 14 Jul 2022 • Xingping Dong, Shengcai Liao, Bo Du, Ling Shao
Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit.
no code implementations • CVPR 2022 • Dongming Wu, Xingping Dong, Ling Shao, Jianbing Shen
To address this, we propose a novel multi-level representation learning approach, which explores the inherent structure of the video content to provide a set of discriminative visual embedding, enabling more effective vision-language semantic alignment.
1 code implementation • CVPR 2021 • Wencheng Han, Xingping Dong, Fahad Shahbaz Khan, Ling Shao, Jianbing Shen
We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on large-scale data.
Ranked #22 on Visual Object Tracking on TrackingNet
no code implementations • 24 Jul 2019 • Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi
This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools.
no code implementations • ECCV 2018 • Xingping Dong, Jianbing Shen
In this paper, a novel triplet loss is proposed to extract expressive deep feature for object tracking by adding it into Siamese network framework instead of pairwise loss for training.
1 code implementation • CVPR 2018 • Wenguan Wang, Jianbing Shen, Xingping Dong, Ali Borji
Salient object detection is then viewed as fine-grained object-level saliency segmentation and is progressively optimized with the guidance of the fixation map in a top-down manner.
no code implementations • CVPR 2018 • Xingping Dong, Jianbing Shen, Wenguan Wang, Yu Liu, Ling Shao, Fatih Porikli
Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process.
no code implementations • 19 May 2017 • Xingping Dong, Jianbing Shen, Dongming Wu, Kan Guo, Xiaogang Jin, Fatih Porikli
In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation.