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Jianfeng Yang

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3 papers

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AAAI Conference 2024 Conference Paper

CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-Scale Geometry

Yingrui Wu
Mingyang Zhao
Keqiang Li
Weize Quan
Tianqi Yu
Jianfeng Yang
Xiaohong Jia
Dong-Ming Yan

This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Normal Distance to address this issue. This not only mitigates the challenge but also facilitates network training and substantially enhances the network robustness against noise. Subsequently, we devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion. This design empowers the network to capture intricate geometric details more effectively and alleviate the ambiguity in scale selection. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on both synthetic and real-world datasets, particularly in scenarios contaminated by noise. Our implementation is available at https://github.com/YingruiWoo/CMG-Net_Pytorch.

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YNIMG Journal 2012 Journal Article

Task by stimulus interactions in brain responses during Chinese character processing

Jianfeng Yang
Xiaojuan Wang
Hua Shu
Jason D. Zevin

In the visual word recognition literature, it is well understood that various stimulus effects interact with behavioral task. For example, effects of word frequency are exaggerated and effects of spelling-to-sound regularity are reduced in the lexical decision task, relative to reading aloud. Neuroimaging studies of reading often examine effects of task and stimulus properties on brain activity independently, but potential interactions between task demands and stimulus effects have not been extensively explored. To address this issue, we conducted lexical decision and symbol detection tasks using stimuli that varied parametrically in their word-likeness, and tested for task by stimulus class interactions. Interactions were found throughout the reading system, such that stimulus selectivity was observed during the lexical decision task, but not during the symbol detection task. Further, the pattern of stimulus selectivity was directly related to task difficulty, so that the strongest brain activity was observed to the most word-like stimuli that required “no” responses, whereas brain activity to words, which elicit rapid and accurate “yes” responses were relatively weak. This is in line with models that argue for task-dependent specialization of brain regions, and contrasts with the notion of task-independent stimulus selectivity in the reading system.

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YNIMG Journal 2011 Journal Article

Left fusiform BOLD responses are inversely related to word-likeness in a one-back task

Xiaojuan Wang
Jianfeng Yang
Hua Shu
Jason D. Zevin

Although its precise functional contribution to reading remains unclear, there is broad consensus that an activity in the left mid-fusiform gyrus is highly sensitive to written words and word-like stimuli. In the current study, we take advantage of a particularity of the Chinese writing system in order to manipulate word-likeness parametrically, from real characters, to pseudo-characters that vary in whether they contain phonological and semantic cues, to artificial stimuli with varying surface similarity to real characters. In a one-back task, BOLD activity in the left mid-fusiform was inversely related to word-likeness, such that the least activity was observed in response to real characters, and the greatest to artificial stimuli that violate the orthotactic constraints of the writing system. One possible explanation for this surprising result is that the short-term memory demands of the one-back task put more pressure on the visual system when other sources of information cannot be used to aid in detecting repeated stimuli. For real characters and, to a lesser extent for pseudo-characters, information about meaning and pronunciation can contribute to performance, whereas artificial stimuli are entirely dependent on visual information. Consistent with this view, functional connectivity analyses revealed a strong positive relationship between left mid-fusiform and other visual areas, whereas areas typically involved in phonological and semantic processing for text were negatively correlated with this region.

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