HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912706637856768 |
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| author | Zhao, Yueran Zhang, Zhang Sun, Chao Wang, Tianze Yue, Chao Li, Nuoran |
| author_facet | Zhao, Yueran Zhang, Zhang Sun, Chao Wang, Tianze Yue, Chao Li, Nuoran |
| contents | Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission. However, as more agents participate, existing frameworks face two key challenges: (1) the participating agents are inherently multi-modal and heterogeneous, and (2) the collaborative framework must be scalable to accommodate new agents. The former requires effective cross-agent feature alignment to mitigate heterogeneity loss, while the latter renders full-parameter training impractical, highlighting the importance of scalable adaptation. To address these issues, we propose Heterogeneous Adaptation (HeatV2X), a scalable collaborative framework. We first train a high-performance agent based on heterogeneous graph attention as the foundation for collaborative learning. Then, we design Local Heterogeneous Fine-Tuning and Global Collaborative Fine-Tuning to achieve effective alignment and interaction among heterogeneous agents. The former efficiently extracts modality-specific differences using Hetero-Aware Adapters, while the latter employs the Multi-Cognitive Adapter to enhance cross-agent collaboration and fully exploit the fusion potential. These designs enable substantial performance improvement of the collaborative framework with minimal training cost. We evaluate our approach on the OPV2V-H and DAIR-V2X datasets. Experimental results demonstrate that our method achieves superior perception performance with significantly reduced training overhead, outperforming existing state-of-the-art approaches. Our implementation will be released soon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction Zhao, Yueran Zhang, Zhang Sun, Chao Wang, Tianze Yue, Chao Li, Nuoran Computer Vision and Pattern Recognition Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission. However, as more agents participate, existing frameworks face two key challenges: (1) the participating agents are inherently multi-modal and heterogeneous, and (2) the collaborative framework must be scalable to accommodate new agents. The former requires effective cross-agent feature alignment to mitigate heterogeneity loss, while the latter renders full-parameter training impractical, highlighting the importance of scalable adaptation. To address these issues, we propose Heterogeneous Adaptation (HeatV2X), a scalable collaborative framework. We first train a high-performance agent based on heterogeneous graph attention as the foundation for collaborative learning. Then, we design Local Heterogeneous Fine-Tuning and Global Collaborative Fine-Tuning to achieve effective alignment and interaction among heterogeneous agents. The former efficiently extracts modality-specific differences using Hetero-Aware Adapters, while the latter employs the Multi-Cognitive Adapter to enhance cross-agent collaboration and fully exploit the fusion potential. These designs enable substantial performance improvement of the collaborative framework with minimal training cost. We evaluate our approach on the OPV2V-H and DAIR-V2X datasets. Experimental results demonstrate that our method achieves superior perception performance with significantly reduced training overhead, outperforming existing state-of-the-art approaches. Our implementation will be released soon. |
| title | HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.10211 |