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Yiqing Hu

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6 papers

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YNIMG Journal 2025 Journal Article

Development of the relationship between visual selective attention and auditory change detection

Yuanjun Kong
Xuye Yuan
Yiqing Hu
Bingkun Li
Dongwei Li
Jialiang Guo
Meirong Sun
Yan Song

Understanding the developmental trajectories of the auditory and visual systems is crucial to elucidate cognitive maturation and its associated relationships, which are essential for effectively navigating dynamic environments. Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. However, the evolution of these processes and their relationship over time remains unclear. In this study, we recorded electroencephalography signals from 118 participants (42 adults and 76 typically developing children) during separate visual localization and auditory-embedded fixation tasks. Further, we employed both ERP analysis and multivariate pattern machine learning to investigate developmental patterns. ERP amplitude and decoding accuracy provided convergent evidence underlying a linear developmental trajectory for visual selective attention and an inverted U-shaped trajectory for auditory change detection from childhood to adulthood. Importantly, our findings confirmed the established association of an N2 pc-MMN in adults using a larger sample size, and further identified a positive correlation between decoding accuracy for visual target location and decoding accuracy for auditory stimulus type specifically in adults. However, both visual-auditory correlation effects were absent in children. Our study provides neurophysiological insights into the distinct developmental trajectories of visual selective attention and auditory change detection. It highlights that the close relationship between individual differences in the two processes emerges alongside their respective maturation and does not become evident until adulthood.

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YNIMG Journal 2023 Journal Article

Prioritizing flexible working memory representations through retrospective attentional strengthening

Dongwei Li
Yiqing Hu
Mengdi Qi
Chenguang Zhao
Ole Jensen
Jing Huang
Yan Song

Previous work has proposed two potential benefits of retrospective attention on working memory (WM): target strengthening and non-target inhibition. It remains unknown which hypothesis contributes to the improved WM performance, yet the neural mechanisms responsible for this attentional benefit are unclear. Here, we recorded electroencephalography (EEG) signals while 33 participants performed a retrospective-cue WM task. Multivariate pattern classification analysis revealed that only representations of target features were enhanced by valid retrospective attention during retention, supporting the target strengthening hypothesis. Further univariate analysis found that mid-frontal theta inter-trial phase coherence (ITPC) and ERP components were modulated by valid retrospective attention and correlated with individual differences and moment-to-moment fluctuations on behavioral outcomes, suggesting that both trait- and state-level variability in attentional preparatory processes influence goal-directed behavior. Furthermore, task-irrelevant target spatial location could be decoded from EEG signals, indicating that enhanced spatial binding of target representation is vital to high WM precision. Importantly, frontoparietal theta-alpha phase-amplitude coupling was increased by valid retrospective attention and predicted the reduced random guessing rates. This long-range connection supported top-down information flow in the engagement of frontoparietal networks, which might organize attentional states to integrate target features. Altogether, these results provide neurophysiological bases that retrospective attention improves WM precision by enhancing flexible target representation and emphasize the critical role of the frontoparietal attentional network in the control of WM representations.

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AAAI Conference 2023 Conference Paper

TaCo: Textual Attribute Recognition via Contrastive Learning

Chang Nie
Yiqing Hu
Yanqiu Qu
Hao Liu
Deqiang Jiang
Bo Ren

As textual attributes like font are core design elements of document format and page style, automatic attributes recognition favor comprehensive practical applications. Existing approaches already yield satisfactory performance in differentiating disparate attributes, but they still suffer in distinguishing similar attributes with only subtle difference. Moreover, their performance drop severely in real-world scenarios where unexpected and obvious imaging distortions appear. In this paper, we aim to tackle these problems by proposing TaCo, a contrastive framework for textual attribute recognition tailored toward the most common document scenes. Specifically, TaCo leverages contrastive learning to dispel the ambiguity trap arising from vague and open-ended attributes. To realize this goal, we design the learning paradigm from three perspectives: 1) generating attribute views, 2) extracting subtle but crucial details, and 3) exploiting valued view pairs for learning, to fully unlock the pre-training potential. Extensive experiments show that TaCo surpasses the supervised counterparts and advances the state-of-the-art remarkably on multiple attribute recognition tasks. Online services of TaCo will be made available.

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AAAI Conference 2023 Conference Paper

The Devil Is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-training

Hao Liu
Xinghua Jiang
Xin Li
Antai Guo
Yiqing Hu
Deqiang Jiang
Bo Ren

The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing interest in the community, owing to the excellent ability of learning visual representation from unlabeled data. Aiming at learning representations with high semantics abstracted, a group of works attempts to reconstruct non-semantic pixels with large-ratio masking strategy, which may suffer from "over-smoothing" problem, while others directly infuse semantics into targets in off-line way requiring extra data. Different from them, we shift the perspective to the Fourier domain which naturally has global perspective and present a new Masked Image Modeling (MIM), termed Geminated Gestalt Autoencoder (Ge^2-AE) for visual pre-training. Specifically, we equip our model with geminated decoders in charge of reconstructing image contents from both pixel and frequency space, where each other serves as not only the complementation but also the reciprocal constraints. Through this way, more robust representations can be learned in the pre-trained encoders, of which the effectiveness is confirmed by the juxtaposing experimental results on downstream recognition tasks. We also conduct several quantitative and qualitative experiments to investigate the learning behavior of our method. To our best knowledge, this is the first MIM work to solve the visual pre-training through the lens of frequency domain.

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AAAI Conference 2022 Conference Paper

TDv2: A Novel Tree-Structured Decoder for Offline Mathematical Expression Recognition

Changjie Wu
Jun Du
Yunqing Li
Jianshu Zhang
Chen Yang
Bo Ren
Yiqing Hu

In recent years, tree decoders become more popular than La- TeX string decoders in the field of handwritten mathematical expression recognition (HMER) as they can capture the hierarchical tree structure of mathematical expressions. However previous tree decoders converted the tree structure labels into a fixed and ordered sequence, which could not make full use of the diversified expression of tree labels. In this study, we propose a novel tree decoder (TDv2) to fully utilize the tree structure labels. Compared with previous tree decoders, this new model does not require a fixed priority for different branches of a node during training and inference, which can effectively improve the model generalization capability. The input and output of the model make full use of the tree structure label, so that there is no need to find the parent node in the decoding process, which simplifies the decoding process and adds a priori information to help predict the node. We verified the effectiveness of each part of the model through comprehensive ablation experiments and attention visualization analysis. On the authoritative CROHME 14/16/19 datasets, our method achieves the state-of-the-art results.

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AAAI Conference 2020 Conference Paper

Accurate Structured-Text Spotting for Arithmetical Exercise Correction

Yiqing Hu
Yan Zheng
Hao Liu
Dequang Jiang
Yinsong Liu
Bo Ren

Correcting arithmetical exercise is a labor intensive and time consuming task for primary school teachers all the time. To reduce their burdens, we propose Arithmetical Exercise Checker (AEC), which is the ﬁrst system that automatically evaluates all arithmetical expressions (AEs) on exercise images. The major challenge is that AE is formed by printed and handwritten texts with particular arithmetical patterns (e. g. , multi-line, fraction). Despite being part of AE, handwritten texts usually lead to zigzag boundaries and tangled rows. What’s worse, AE may be arithmetical incorrect, which makes the contextual information less valuable for recognition. To tackle these problems, we introduce integrated detection, recognition and evaluation branches by leveraging AE’s intrinsic features, namely 1) boundary indistinctive, 2) locally relevant patterns and 3) globally irrelevant symbols. Experimental results demonstrate that AEC yields a 93. 72% correction accuracy on 40 kinds of mainstream primary arithmetical exercises. So far, the online service of AEC processes 75, 000 arbitrary exercises on average per day, and already reduced the burden of over 1, 000, 000 users. AEC shows the bene- ﬁts for implementing an vision-based system as a way to aid teachers in reducing reduplicative tasks.

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