Search Results for author: Zehao Xiao

Found 10 papers, 5 papers with code

Any-Shift Prompting for Generalization over Distributions

no code implementations15 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.

Language Modelling

Learning Variational Neighbor Labels for Test-Time Domain Generalization

no code implementations8 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.

Domain Generalization Variational Inference

ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion

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.

Few-Shot Learning

Learning to Generalize across Domains on Single Test Samples

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.

Bayesian Inference Domain Generalization +1

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

1 code implementation9 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.

Bayesian Inference Domain Generalization

Variational Invariant Learning for Bayesian Domain Generalization

no code implementations1 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.

Domain Generalization

Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network

no code implementations3 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.

Crowd Counting Density Estimation

In Defense of Single-column Networks for Crowd Counting

no code implementations18 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.

Crowd Counting Data Augmentation +1

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