Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection

• URL: http://arxiv.org/abs/2503.06948v1
• Date: Mon, 10 Mar 2025 05:53:30 GMT
• Title: Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection
• Abstract summary: Existing multimodal UAV object detection methods often overlook the impact of semantic gaps between modalities.<n>We propose a Large Language Model (LLM) guided Progressive feature Alignment Network called LPANet.<n>We show that our approach outperforms state-of-the-art multimodal UAV object detectors.
• Score: 21.16636753446158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multimodal UAV object detection methods often overlook the impact of semantic gaps between modalities, which makes it difficult to achieve accurate semantic and spatial alignments, limiting detection performance. To address this problem, we propose a Large Language Model (LLM) guided Progressive feature Alignment Network called LPANet, which leverages the semantic features extracted from a large language model to guide the progressive semantic and spatial alignment between modalities for multimodal UAV object detection. To employ the powerful semantic representation of LLM, we generate the fine-grained text descriptions of each object category by ChatGPT and then extract the semantic features using the large language model MPNet. Based on the semantic features, we guide the semantic and spatial alignments in a progressive manner as follows. First, we design the Semantic Alignment Module (SAM) to pull the semantic features and multimodal visual features of each object closer, alleviating the semantic differences of objects between modalities. Second, we design the Explicit Spatial alignment Module (ESM) by integrating the semantic relations into the estimation of feature-level offsets, alleviating the coarse spatial misalignment between modalities. Finally, we design the Implicit Spatial alignment Module (ISM), which leverages the cross-modal correlations to aggregate key features from neighboring regions to achieve implicit spatial alignment. Comprehensive experiments on two public multimodal UAV object detection datasets demonstrate that our approach outperforms state-of-the-art multimodal UAV object detectors.

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