activity
20142024
most citedTransReID: Transformer-based Object Re-Identification

95 citations · 210 across the 17 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 4 cited

KVT: k-NN Attention for Boosting Vision Transformers

Pichao Wang, Xue Wang, Fan Wang +4

Convolutional Neural Networks (CNNs) have dominated computer vision for years, due to its ability in capturing locality and translation invariance. Recently, many vision transforme…

cs.CV2021★ 1 cited

An Empirical Study of Vehicle Re-Identification on the AI City Challenge

Hao Luo, Weihua Chen, Xianzhe Xu +7

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synt…

cs.CV2021★ 2 cited

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

Chong Liu, Yuqi Zhang, Hao Luo +6

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3…

cs.CV2021★ 3 cited

Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation

Shuning Chang, Pichao Wang, Fan Wang +2

Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection. Most previous works focus on captu…

cs.CV2021★ 95 cited

TransReID: Transformer-based Object Re-Identification

Shuting He, Hao Luo, Pichao Wang +3

Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved grea…

cs.CV2021★ 12 cited

1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

Fei Du, Bo Xu, Jiasheng Tang +3

We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniq…