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Researcher profile

Jianwei Hu

Jianwei Hu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computation and LanguageComputer VisionMachine LearningMethodologyphysics.soc-ph

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Published work

3 published item(s)

preprint2026arXiv

CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID

Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data privacy. However, this task is significantly challenged by the inherent stylistic gaps across decentralized clients. Without global supervision, models easily succumb to shortcut learning where representations overfit to domain specific camera biases rather than universal identity features. We propose CO-EVO, a novel federated framework that resolves this semantic-style conflict through a co-evolutionary mechanism. On the semantic side, Camera-Invariant Semantic Anchoring (CSA) learns identity prompts with cross-camera consistency to establish purified and domain-agnostic anchors that filter out local imaging noise. On the visual side, Global Style Diversification (GSD), powered by a Global Camera-Style Bank (GCSB), synthesizes realistic perturbations to expand the visual boundaries of training data. The core of CO-EVO is its co-evolutionary loop where purified anchors act as gravitational centers to guide the image encoder toward robust anatomical attributes amidst diverse style variations. Extensive experiments demonstrate that CO-EVO achieves state-of-the-art (SOTA) performance, proving that the synergy between semantic purification and style expansion is essential for robust cross-domain generalization. Our code is available at: https://github.com/NanYiyuzurn/ACL-LGPS-2026.

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preprint2020arXiv

Detection of the number of principal components by extended AIC-type method

Estimating the number of principal components is one of the fundamental problems in many scientific fields such as signal processing (or the spiked covariance model). In this paper, we first demonstrate that, for fixed $p$, any penalty term of the form $k'(p-k'/2+1/2)C_n$ may lead to an asymptotically consistent estimator under the condition that $C_n\to\infty$ and $C_n/n\to0$. We also extend our results to the case $n,p\to\infty$, with $p/n\to c>0$. In this case, for $k=o(n^{\frac{1}{3}})$, we first investigate the limiting laws for the leading eigenvalues of the sample covariance matrix $S_n$ under the condition that $λ_k>1+\sqrt{c}$. At low SNR, since the AIC tends to underestimate the number of signals $k$, the AIC should be re-defined in this case. As a natural extension of the AIC for fixed $p$, we propose the extended AIC (EAIC), i.e., the AIC-type method with tuning parameter $γ=φ(c)=1/2+\sqrt{1/c}-\log(1+\sqrt{c})/c$, and demonstrate that the EAIC-type method, i.e., the AIC-type method with tuning parameter $γ>φ(c)$, can select the number of signals $k$ consistently. In the following two cases, (1) $p$ fixed, $n\to\infty$, (2) $n,p\to\infty$ with $p/n\to 0$, if the AIC is defined as the degeneration of the EAIC in the case $n,p\to\infty$ with $p/n\to c>0$, i.e., $γ=\lim_{c\rightarrow 0+0}φ(c)=1$, then we have essentially demonstrated that, to achieve the consistency of the AIC-type method in the above two cases, $γ>1$ is required. Moreover, we show that the EAIC-type method is essentially tuning-free and outperforms the well-known KN estimator proposed in Kritchman and Nadler (2008) and the BCF estimator proposed in Bai, Choi and Fujikoshi (2018). Numerical studies indicate that the proposed method works well.

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preprint2017arXiv

Communicating with sentences: A multi-word naming game model

Naming game simulates the process of naming an object by a single word, in which a population of communicating agents can reach global consensus asymptotically through iteratively pair-wise conversations. We propose an extension of the single-word model to a multi-word naming game (MWNG), simulating the case of describing a complex object by a sentence (multiple words). Words are defined in categories, and then organized as sentences by combining them from different categories. We refer to a formatted combination of several words as a pattern. In such an MWNG, through a pair-wise conversation, it requires the hearer to achieve consensus with the speaker with respect to both every single word in the sentence as well as the sentence pattern, so as to guarantee the correct meaning of the saying, otherwise, they fail reaching consensus in the interaction. We validate the model in three typical topologies as the underlying communication network, and employ both conventional and man-designed patterns in performing the MWNG.

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