1 code implementation • 9 Apr 2024 • Jiayi Shen, Cheems Wang, Zehao Xiao, Nanne van Noord, Marcel Worring
This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks.
no code implementations • 15 Feb 2024 • Zehao Xiao, Jiayi Shen, Mohammad Mahdi Derakhshani, Shengcai Liao, Cees G. M. Snoek
To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism.
no code implementations • 8 Jul 2023 • Sameer Ambekar, Zehao Xiao, Jiayi Shen, XianTong Zhen, Cees G. M. Snoek
We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels.
1 code implementation • NeurIPS 2023 • Yingjun Du, Zehao Xiao, Shengcai Liao, Cees Snoek
Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.
1 code implementation • 22 Feb 2023 • Zehao Xiao, XianTong Zhen, Shengcai Liao, Cees G. M. Snoek
In this paper, we propose energy-based sample adaptation at test time for domain generalization.
1 code implementation • 10 Oct 2022 • Jiayi Shen, Zehao Xiao, XianTong Zhen, Cees G. M. Snoek, Marcel Worring
To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks.
1 code implementation • ICLR 2022 • Zehao Xiao, XianTong Zhen, Ling Shao, Cees G. M. Snoek
We leverage a meta-learning paradigm to learn our model to acquire the ability of adaptation with single samples at training time so as to further adapt itself to each single test sample at test time.
Ranked #1 on Domain Adaptation on PACS
1 code implementation • 9 May 2021 • Zehao Xiao, Jiayi Shen, XianTong Zhen, Ling Shao, Cees G. M. Snoek
Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data.
no code implementations • 1 Jan 2021 • Zehao Xiao, Jiayi Shen, XianTong Zhen, Ling Shao, Cees G. M. Snoek
In the probabilistic modeling framework, we introduce a domain-invariant principle to explore invariance across domains in a unified way.
no code implementations • 3 Mar 2019 • Xiaolong Jiang, Zehao Xiao, Baochang Zhang, Xian-Tong Zhen, Xian-Bin Cao, David Doermann, Ling Shao
In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.
no code implementations • 18 Aug 2018 • Ze Wang, Zehao Xiao, Kai Xie, Qiang Qiu, Xian-Tong Zhen, Xian-Bin Cao
Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering.