289 citations · 892 across the 15 of their papers we have counts for
16 papers
End-to-End Human Instance Matting
Qinglin Liu, Shengping Zhang, Quanling Meng +3
Human instance matting aims to estimate an alpha matte for each human instance in an image, which is extremely challenging and has rarely been studied so far. Despite some efforts…
Towards Unified Token Learning for Vision-Language Tracking
Yaozong Zheng, Bineng Zhong, Qihua Liang +3
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. Trad…
Visualizing and Understanding Patch Interactions in Vision Transformer
Jie Ma, Yalong Bai, Bineng Zhong +3
Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly thro…
Long-Range Feature Propagating for Natural Image Matting
Qinglin Liu, Haozhe Xie, Shengping Zhang +2
Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknow…
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
Zikai Zhang, Bineng Zhong, Shengping Zhang +3
A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness m…
Learning to Filter: Siamese Relation Network for Robust Tracking
Siyuan Cheng, Bineng Zhong, Guorong Li +4
Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we p…
Projection & Probability-Driven Black-Box Attack
Jie Li, Rongrong Ji, Hong Liu +4
Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer f…
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Jiahua Dong, Yang Cong, Gan Sun +2
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer…
Siamese Box Adaptive Network for Visual Tracking
Zedu Chen, Bineng Zhong, Guorong Li +2
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. Unfor…
Residual Non-local Attention Networks for Image Restoration
Yulun Zhang, Kunpeng Li, Kai Li +2
In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted ima…
Representative Task Self-selection for Flexible Clustered Lifelong Learning
Gan Sun, Yang Cong, Qianqian Wang +2
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights.…
Residual Dense Network for Image Restoration
Yulun Zhang, Yapeng Tian, Yu Kong +2
Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not ma…
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Yulun Zhang, Kunpeng Li, Kai Li +3
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train…
Predictive Local Smoothness for Stochastic Gradient Methods
Jun Li, Hongfu Liu, Bineng Zhong +2
Stochastic gradient methods are dominant in nonconvex optimization especially for deep models but have low asymptotical convergence due to the fixed smoothness. To address this pro…
Residual Dense Network for Image Super-Resolution
Yulun Zhang, Yapeng Tian, Yu Kong +2
A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN…
An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition
Yan Yan, Hanzi Wang, Cuihua Li +2
In this paper, an effective unconstrained correlation filter called Uncon- strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to robust face recognition. Comp…