1 code implementation • 14 May 2021 • Chong Liu, Yuqi Zhang, Hao Luo, Jiasheng Tang, Weihua Chen, Xianzhe Xu, Fan Wang, Hao Li, Yi-Dong Shen
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions.
1 code implementation • 20 May 2021 • Hao Luo, Weihua Chen, Xianzhe Xu, Jianyang Gu, Yuqi Zhang, Chong Liu, Yiqi Jiang, Shuting He, Fan Wang, Hao Li
We mainly focus on four points, i. e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.
1 code implementation • 5 Jul 2021 • Yuqi Zhang, Qian Qi, Chong Liu, Weihua Chen, Fan Wang, Hao Li, Rong Jin
In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric.
1 code implementation • 15 Jun 2023 • Yuqi Zhang, Qi Qian, Hongsong Wang, Chong Liu, Weihua Chen, Fan Wang
In particular, the plain GCR is extended for cross-camera retrieval and an improved feature propagation formulation is presented to leverage affinity relationships across different cameras.
1 code implementation • 15 Jun 2023 • Chong Liu, Yuqi Zhang, Hongsong Wang, Weihua Chen, Fan Wang, Yan Huang, Yi-Dong Shen, Liang Wang
Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words.
1 code implementation • ICCV 2021 • Chong Liu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang, ZongYuan Ge, Yi-Dong Shen
Then, we cross-connect the key views of different scenes to construct augmented scenes.
Ranked #38 on Vision and Language Navigation on VLN Challenge
2 code implementations • 13 Dec 2021 • Chong Liu, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, Leyu Lin
State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations.
1 code implementation • NeurIPS 2021 • Cristopher Salvi, Maud Lemercier, Chong Liu, Blanka Hovarth, Theodoros Damoulas, Terry Lyons
Stochastic processes are random variables with values in some space of paths.
1 code implementation • 8 Aug 2022 • Xiaoyang Liu, Chong Liu, Pinzheng Wang, Rongqin Zheng, Lixin Zhang, Leyu Lin, Zhijun Chen, Liangliang Fu
To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance.
no code implementations • 8 Jun 2019 • Chong Liu, Yu-Xiang Wang
Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today.
no code implementations • CVPR 2020 • Chong Liu, Xiaojun Chang, Yi-Dong Shen
To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras.
no code implementations • 27 Jul 2020 • Changsheng Li, Chong Liu, Lixin Duan, Peng Gao, Kai Zheng
In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem.
no code implementations • 6 Nov 2020 • Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
The Private Aggregation of Teacher Ensembles (PATE) framework is one of the most promising recent approaches in differentially private learning.
no code implementations • 16 Jan 2020 • Philipp Harms, Chong Liu, Ariel Neufeld
In this paper we study arbitrage theory of financial markets in the absence of a num\'eraire both in discrete and continuous time.
no code implementations • 16 Nov 2022 • Chong Liu, Yu-Xiang Wang
We consider the problem of global optimization with noisy zeroth order oracles - a well-motivated problem useful for various applications ranging from hyper-parameter tuning for deep learning to new material design.
no code implementations • 15 Jan 2023 • Ben Hoar, Roshini Ramachandran, Marc Levis, Erin Sparck, Ke wu, Chong Liu
Often, student opinions are gathered with a general comment section that solicits their feelings towards their courses without polling specifics about course contents.
no code implementations • 16 Feb 2023 • Jing Xu, Dandan song, Chong Liu, Siu Cheung Hui, Fei Li, Qiang Ju, Xiaonan He, Jian Xie
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
no code implementations • 26 Feb 2023 • Chong Liu, Ming Yin, Yu-Xiang Wang
It achieves a near-optimal $\sqrt{T}$ regret for problems that the best-known regret is almost linear in time horizon $T$.
no code implementations • 24 Apr 2023 • Anastasis Kratsios, Chong Liu, Matti Lassas, Maarten V. de Hoop, Ivan Dokmanić
Motivated by the developing mathematics of deep learning, we build universal functions approximators of continuous maps between arbitrary Polish metric spaces $\mathcal{X}$ and $\mathcal{Y}$ using elementary functions between Euclidean spaces as building blocks.
no code implementations • 15 Aug 2023 • Chong Liu, Xiaoyang Liu, Ruobing Xie, Lixin Zhang, Feng Xia, Leyu Lin
A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels.
no code implementations • 24 Aug 2023 • Yining Ye, Xin Cong, Shizuo Tian, Yujia Qin, Chong Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun
Central to the development of rationality is the construction of an internalized utility judgment, capable of assigning numerical utilities to each decision.
no code implementations • 28 Sep 2023 • Chong Liu, Xiaoyang Liu, Lixin Zhang, Feng Xia, Leyu Lin
Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method.
no code implementations • 27 Oct 2023 • Wenqian Xing, Jungho Lee, Chong Liu, Shixiang Zhu
This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space.
no code implementations • 3 Nov 2023 • Chuanhao Li, Chong Liu, Yu-Xiang Wang
Federated optimization studies the problem of collaborative function optimization among multiple clients (e. g. mobile devices or organizations) under the coordination of a central server.