591 citations · 666 across the 18 of their papers we have counts for
18 papers
Group Distributionally Robust Dataset Distillation with Risk Minimization
Saeed Vahidian, Mingyu Wang, Jianyang Gu +3
Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the…
Efficient Dataset Distillation via Minimax Diffusion
Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev +4
Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the origi…
DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching
Yanqing Liu, Jianyang Gu, Kai Wang +4
Dataset distillation plays a crucial role in creating compact datasets with similar training performance compared with original large-scale ones. This is essential for addressing t…
Can pre-trained models assist in dataset distillation?
Yao Lu, Xuguang Chen, Yuchen Zhang +7
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training. Meanwhile,…
Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification
Jianyang Gu, Hao Luo, Kai Wang +3
Unsupervised domain adaptive person re-identification (Re-ID) methods alleviate the burden of data annotation through generating pseudo supervision messages. However, real-world Re…
Dataset Quantization
Daquan Zhou, Kai Wang, Jianyang Gu +5
State-of-the-art deep neural networks are trained with large amounts (millions or even billions) of data. The expensive computation and memory costs make it difficult to train them…
Summarizing Stream Data for Memory-Constrained Online Continual Learning
Jianyang Gu, Kai Wang, Wei Jiang +1
Replay-based methods have proved their effectiveness on online continual learning by rehearsing past samples from an auxiliary memory. With many efforts made on improving training…
MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
Jianyang Gu, Kai Wang, Hao Luo +6
Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the r…
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
Ziheng Qin, Kai Wang, Zangwei Zheng +8
Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training. This could lead to…
DiM: Distilling Dataset into Generative Model
Kai Wang, Jianyang Gu, Daquan Zhou +3
Dataset distillation reduces the network training cost by synthesizing small and informative datasets from large-scale ones. Despite the success of the recent dataset distillation…
DREAM: Efficient Dataset Distillation by Representative Matching
Yanqing Liu, Jianyang Gu, Kai Wang +3
Dataset distillation aims to synthesize small datasets with little information loss from original large-scale ones for reducing storage and training costs. Recent state-of-the-art…
SoccerNet 2022 Challenges Results
Silvio Giancola, Anthony Cioppa, Adrien Deliège +91
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks:…
Dynamic Gradient Reactivation for Backward Compatible Person Re-identification
Xiao Pan, Hao Luo, Weihua Chen +6
We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing featu…
MM-SEAL: A Large-scale Video Dataset of Multi-person Multi-grained Spatio-temporally Action Localization
Shimin Chen, Wei Li, Chen Chen +4
In this paper, we introduce a novel large-scale video dataset dubbed MM-SEAL for multi-person multi-grained spatio-temporal action localization among human daily life. We are the f…
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…
1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification
Jianyang Gu, Hao Luo, Weihua Chen +6
This paper presents our proposed methods for domain adaptive pedestrian re-identification (Re-ID) task in Visual Domain Adaptation Challenge (VisDA-2020). Considering the large gap…
A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification
Hao Luo, Wei Jiang, Youzhi Gu +4
This study explores a simple but strong baseline for person re-identification (ReID). Person ReID with deep neural networks has progressed and achieved high performance in recent y…
ZJUNlict Extended Team Description Paper for RoboCup 2019
Zheyuan Huang, Lingyun Chen, Jiacheng Li +6
For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort…