TGraphX融合CNN与GNN，用张量节点提升视觉推理能力
TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning
- 用CNN生成保留空间语义的多维节点张量
- 通过1×1卷积实现结构保持的消息传递
- 适合需要精细关系建模的视觉任务
TGraphX提出一种新型深度学习范式，将卷积神经网络（CNN）与图神经网络（GNN）统一，以增强视觉推理任务。传统CNN擅长从图像中提取丰富空间特征，但缺乏建模物体间关系的能力；而常规GNN通常依赖扁平化节点特征，丢失关键空间信息。TGraphX通过CNN生成多维节点特征（如(3×128×128)张量），保留局部空间语义。这些空间感知节点参与图结构，消息传递采用1×1卷积，融合邻近特征并维持结构完整性。此外，使用带残差连接的深层CNN聚合器，稳健地优化融合消息，确保梯度稳定和端到端可训练性。该方法不仅弥合了空间特征提取与关系推理之间的差距，还在目标检测精炼和集成推理上取得显著提升。
原文摘要 · Abstract (English)
TGraphX presents a novel paradigm in deep learning by unifying convolutional neural networks (CNNs) with graph neural networks (GNNs) to enhance visual reasoning tasks. Traditional CNNs excel at extracting rich spatial features from images but lack the inherent capability to model inter-object relationships. Conversely, conventional GNNs typically rely on flattened node features, thereby discarding vital spatial details. TGraphX overcomes these limitations by employing CNNs to generate multi-dimensional node features (e.g., (3*128*128) tensors) that preserve local spatial semantics. These spatially aware nodes participate in a graph where message passing is performed using 1*1 convolutions, which fuse adjacent features while maintaining their structure. Furthermore, a deep CNN aggregator with residual connections is used to robustly refine the fused messages, ensuring stable gradient flow and end-to-end trainability. Our approach not only bridges the gap between spatial feature extraction and relational reasoning but also demonstrates significant improvements in object detection refinement and ensemble reasoning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。