Dual Sparse Aggregation Transformer for Multispectral Object Detection
Transformer-based approaches have obtained excellent performance in multispectral object detection tasks due to their ability to model long-range dependencies and capture complementary information. However, previous transformer-based multispectral detection methods tend to use all available tokens for similarity calculation, which results in redundant information interaction from irrelevant areas, leading to degraded detection performance. To overcome this challenge, we propose a novel Dual Sparse Aggregation Transformer (DSAFormer) for multispectral object detection, which consists of a Dual Sparse Transformer (DSFormer) and a Learnable Addition Fusion Block (LAFB). Specifically, the DSF...
| Authors | Wencong Wu, Xiuwei Zhang, Hanlin Yin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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