activity
20172023
most citedLasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking

300 citations · 1.2k across the 44 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 3 cited

MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention

Xixi Wang, Xiao Wang, Bo Jiang +2

Aggregating multi-modality data to obtain reliable data representation attracts more and more attention. Recent studies demonstrate that Transformer models usually work well for mu…

cs.MM2021

On the Robustness of "Robust reversible data hiding scheme based on two-layer embedding strategy"

Wen Yin, Longfei Ke, Zhaoxia Yin +2

In the paper "Robust reversible data hiding scheme based on two-layer embedding strategy" published in INS recently, Kumar et al. proposed a robust reversible data hiding (RRDH) sc…

cs.CV2021

Tracking by Joint Local and Global Search: A Target-aware Attention based Approach

Xiao Wang, Jin Tang, Bin Luo +3

Tracking-by-detection is a very popular framework for single object tracking which attempts to search the target object within a local search window for each frame. Although such l…

cs.CV2021★ 300 cited

LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking

Chenglong Li, Wanlin Xue, Yaqing Jia +4

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential fo…

cs.CV2021

Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking

Xiao Wang, Zhe Chen, Jin Tang +4

Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions a…

cs.CV2021★ 2 cited

PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack

Jie Wang, Zhaoxia Yin, Jin Tang +2

The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs). Howev…