Author name cluster

Junyi Chen

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4 papers

2 author rows

EAAI Journal 2026 Journal Article

A robust and interpretable framework for sports activity recognition based on wearable sensor signals and image representations

Jian Li
Yibo Fan
Junhui Gong
Junyi Chen
Ruoyu Chen
Wenyan Zhang
Yuliang Zhao

Human activity recognition (HAR) using wearable sensors has advanced rapidly, improving the precision of complex movement identification. However, existing methods rely on single-modal time-series features, limiting spatiotemporal representation and global dependency capture. This hinders dynamic characterization and reduces recognition accuracy. To overcome these limitations, this paper proposes a novel multimodal fusion deep learning approach to enhance complex action recognition. First, we adopt a multimodal input strategy that integrates time-series data and Gramian Angular Difference Field (GADF) images to comprehensively capture the spatiotemporal characteristics of motion data. Second, we design a dual-stream feature fusion network, where Bidirectional Gated Recurrent Unit (BiGRU) combined with a multi-head self-attention mechanism (MSA) is employed to extract time-series features, while Efficient Channel Attention (ECA) and residual block are utilized to enhance image feature representation, effectively leveraging the complementary information across modalities. Finally, we introduce an interpretable analysis method based on submodule optimization, enabling cross-modal attribution analysis to identify key regions contributing to model decisions for both time-series and image features. Experimental results demonstrate that the proposed method achieves an accuracy of 96. 88% in a 16-class sports activity recognition task, significantly outperforming traditional machine learning methods and existing deep learning models. This study provides an effective solution for complex action recognition and lays a technological foundation for real-time motion monitoring in wearable smart devices and broader HAR applications.

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AAAI Conference 2025 Conference Paper

GigaGS: 3D Gaussian Based Planar Representation for Large-Scene Surface Reconstruction

Junyi Chen
Weicai Ye
Yifan Wang
Danpeng Chen
Di Huang
Wanli Ouyang
Guofeng Zhang
Yu Qiao

3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. Previous methods adapt it to obtaining surfaces of either individual 3D objects or within limited scenes. In this paper, we make the first attempt to tackle the challenging task of large-scale scene surface reconstruction. This task is particularly difficult due to the high GPU memory consumption, different levels of details for geometric representation, and noticeable inconsistencies in appearance. To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS. GigaGS first applies a partitioning strategy based on the mutual visibility of spatial regions, which effectively grouping cameras for parallel processing. To enhance the quality of the surface, we also propose novel multi-view photometric and geometric consistency constraints based on Level-of-Detail representation. In doing so, our method can reconstruct detailed surface structures. Comprehensive experiments are conducted on various datasets. The consistent improvement demonstrates the superiority of GigaGS.

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ICLR Conference 2025 Conference Paper

Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction

Junyi Chen
Di Huang
Weicai Ye
Wanli Ouyang
Tong He 0001

Spatial intelligence is the ability of a machine to perceive, reason, and act in three dimensions within space and time. Recent advancements in large-scale auto-regressive models have demonstrated remarkable capabilities across various reasoning tasks. However, these models often struggle with fundamental aspects of spatial reasoning, particularly in answering questions like "Where am I?" and "What will I see?". While some attempts have been done, existing approaches typically treat them as separate tasks, failing to capture their interconnected nature. In this paper, we present **G**enerative **S**patial **T**ransformer (GST), a novel auto-regressive framework that jointly addresses spatial localization and view prediction. Our model simultaneously estimates the camera pose from a single image and predicts the view from a new camera pose, effectively bridging the gap between spatial awareness and visual prediction. The proposed innovative camera tokenization method enables the model to learn the joint distribution of 2D projections and their corresponding spatial perspectives in an auto-regressive manner. This unified training paradigm demonstrates that joint optimization of pose estimation and novel view synthesis leads to improved performance in both tasks, for the first time, highlighting the inherent relationship between spatial awareness and visual prediction.

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AAAI Conference 2024 Conference Paper

EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE

Junyi Chen
Longteng Guo
Jia Sun
Shuai Shao
Zehuan Yuan
Liang Lin
Dongyu Zhang

Building scalable vision-language models to learn from diverse, multimodal data remains an open challenge. In this paper, we introduce an Efficient Vision-languagE foundation model, namely EVE, which is one unified multimodal Transformer pre-trained solely by one unified pre-training task. Specifically, EVE encodes both vision and language within a shared Transformer network integrated with modality-aware sparse Mixture-of-Experts (MoE) modules, which capture modality-specific information by selectively switching to different experts. To unify pre-training tasks of vision and language, EVE performs masked signal modeling on image-text pairs to reconstruct masked signals, i.e., image pixels and text tokens, given visible signals. This simple yet effective pre-training objective accelerates training by 4x compared to the model pre-trained with Image-Text Contrastive and Image-Text Matching losses. Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed. Despite its simplicity, EVE achieves state-of-the-art performance on various vision-language downstream tasks, including visual question answering, visual reasoning, and image-text retrieval.

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