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
• Authors: Wentao Wu, Chenglong Li, Xiao Wang, Bin Luo, Qi Liu,
• 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.

Related papers

• IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification [60.38841251693781]
  We propose a novel framework to generate robust multi-modal object ReIDs. Our framework uses Modal Prefixes and InverseNet to integrate multi-modal information with semantic guidance from inverted text. Experiments on three multi-modal object ReID benchmarks demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2025-03-13T13:00:31Z)
• Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment [21.36633828492347]
  Cross-Domain Multi-Modal Few-Shot Object Detection (CDMM-FSOD)<n>We introduce a meta-learning-based framework designed to leverage rich textual semantics as an auxiliary modality to achieve effective domain adaptation.<n>We evaluate the proposed method on common cross-domain object detection benchmarks and demonstrate that it significantly surpasses existing few-shot object detection approaches.
  arXiv  Detail & Related papers  (2025-02-23T06:59:22Z)
• Learning Spatial-Semantic Features for Robust Video Object Segmentation [108.045326229865]
  We propose a robust video object segmentation framework equipped with spatial-semantic features and discriminative object queries. We show that the proposed method set a new state-of-the-art performance on multiple datasets.
  arXiv  Detail & Related papers  (2024-07-10T15:36:00Z)
• Cross-domain Multi-modal Few-shot Object Detection via Rich Text [21.36633828492347]
  Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks.<n>We study the Cross-Domain few-shot generalization of MM-OD (CDMM-FSOD) and propose a meta-learning based multi-modal few-shot object detection method.
  arXiv  Detail & Related papers  (2024-03-24T15:10:22Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• Position-Aware Contrastive Alignment for Referring Image Segmentation [65.16214741785633]
  We present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment.
  arXiv  Detail & Related papers  (2022-12-27T09:13:19Z)
• Support-set based Multi-modal Representation Enhancement for Video Captioning [121.70886789958799]
  We propose a Support-set based Multi-modal Representation Enhancement (SMRE) model to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal interactions in a self-supervised way.
  arXiv  Detail & Related papers  (2022-05-19T03:40:29Z)
• Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation [87.49579477873196]
  We first design a two-stream encoder to extract CNN-based visual features and transformer-based linguistic features hierarchically. A vision-language mutual guidance (VLMG) module is inserted into the encoder multiple times to promote the hierarchical and progressive fusion of multi-modal features. In order to promote the temporal alignment between frames, we propose a language-guided multi-scale dynamic filtering (LMDF) module.
  arXiv  Detail & Related papers  (2022-03-30T01:06:13Z)
• MPI: Multi-receptive and Parallel Integration for Salient Object Detection [17.32228882721628]
  The semantic representation of deep features is essential for image context understanding. In this paper, a novel method called MPI is proposed for salient object detection. The proposed method outperforms state-of-the-art methods under different evaluation metrics.
  arXiv  Detail & Related papers  (2021-08-08T12:01:44Z)
• Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN [117.80737222754306]
  We present a novel universal object detector called Universal-RCNN. We first generate a global semantic pool by integrating all high-level semantic representation of all the categories. An Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN.
  arXiv  Detail & Related papers  (2020-02-18T07:57:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.