Organizations: School of Automation, Southeast University, Nanjing, China · Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China · School of Computer Science and Engineering, University of New South Wales, Sydney, Australia · School of Sports Training, Tianjin University of Sport, Tianjin, China · Tianjin University, Tianjin, China · National University of Defense Technology, Changsha, China
Air-ground cross-view referring person detection is a necessary component in the language-to-perception-to-control chain of collective embodied intelligence, grounding a language command into the same physical target before ground and aerial agents can coordinate downstream actions. Existing referring expression comprehension and open-vocabulary grounding methods do not jointly account for cross-view identity consistency, making them insufficient for Air-Ground Cross-View Referring Person Detection (AGCV-RPD), which involves similar pedestrian distractors, weak aerial appearance cues, and cross-view identity consistency. To study this problem, we introduce Air-Ground Paired Identity-Aware Referring (A-PAIR), the first comprehensive AGCV-RPD benchmark, containing 22,137 cross-view referring samples. To construct A-PAIR efficiently, we propose Factorized Annotation and Referential Alignment (FARA), a semi-automatic annotation framework that generates factorized referring descriptions and identity-consistency supervision at reduced cost. We propose Identity-Consistent Referring Grounding (ICRG), a framework that combines factorized referential grounding, candidate-completeness supervision, and cross-view consistency calibration for joint air-ground pair selection. ICRG improves ground, aerial, and pair-level detection over strong baselines, increasing pair F1 from 16.65% to 22.28%. These results show that AGCV-RPD requires paired detection and identity-consistent reasoning.
Figures & tables
Figure 1: AGCV-RPD task. The model detects the same referred person in paired ground and aerial views.
Figure 2: FARA annotation pipeline. The pipeline constructs A-PAIR from paired G2APS observations with factorized descriptions and identity labels.
Dataset
Lang.
Aerial
Ground
Air-Ground Pair
Same-ID Boxes
ID Aux.
Pair Eval.
RefCOCO ( Yu et al. 2016 )
✓
-
✓
-
-
-
-
RefCOCO+ ( Yu et al. 2016 )
✓
-
✓
-
-
-
-
RefCOCOg ( Mao et al. 2016 )
✓
-
✓
-
-
-
-
RefDrone ( Sun et al. 2026 )
✓
✓
-
-
-
-
-
VisDrone ( Du et al. 2019 )
-
✓
-
-
-
-
-
UAVDT ( Du et al. 2018 )
-
✓
-
-
-
-
-
Table 1: Benchmark comparison. A-PAIR covers language grounding, paired air-ground views, same-identity boxes, and pair-level evaluation.
Split
Samples
Pairs
Gnd. img.
Aer. img.
Train
15,497
6,672
6,672
3,530
Val
2,213
279
279
69
Test
4,427
637
637
292
Total
22,137
7,588
7,588
3,891
Table 2: A-PAIR split statistics.
Figure 3: ICRG framework. It combines factorized grounding, candidate completion, and cross-view consistency.
Method
Ground
Aerial
Pair
F1 i
Acc i
F1 img
Acc img
F1 i
Acc i
F1 img
Acc img
Acc
F1
TransVG ( Deng et al. 2021 )
2.53
1.28
4.94
2.53
0.05
0.02
0.09
0.05
0.00
0.00
PropVG ( Dai et al. 2025 )
1.49
0.75
2.94
1.49
0.00
0.00
0.00
0.00
0.00
0.00
Vision-R1 ( Zhan et al. 2026 )
1.92
0.97
3.77
1.92
0.00
0.00
0.00
0.00
0.00
0.00
SOREC-PIZA ( Goto et al. 2025 )
13.96
7.50
24.50
13.96
7.43
3.86
13.84
7.43
1.78
3.51
GDINO-B ( Liu et al. 2024 )
26.74
15.44
42.20
26.74
12.02
6.39
21.46
12.02
5.74
10.85
Table 3: Main results on A-PAIR. All metrics are percentages; bold and italics mark the best and second-best results.
Fact.
Cand.
Cons.
Ground
Aerial
Pair
F1 i
F1 img
F1 i
F1 img
Acc
F1
-
-
-
7.32
13.65
19.91
33.21
1.83
3.60
✓
-
-
34.88
51.72
20.83
34.47
11.66
20.88
✓
✓
-
36.12
53.07
21.82
35.82
12.24
21.82
✓
✓
✓
35.17
52.04
21.89
35.92
12.54
22.28
Table 4: Ablation results on A-PAIR. Columns indicate enabled supervision signals under the same protocol.
Figure 4: Pair-level IoU-quality decomposition. Each cell reports the percentage of A-PAIR test samples in a ground-aerial IoU-quality bin.
Figure 5: Ground versus pair performance. Each point shows one method by Ground F1 i and Pair F1 on A-PAIR.
Aerial-Ground Person Re-Identification (AGPReID) remains highly challenging due to drastic viewpoint variations between drones and fixed cameras. Existing methods typically follow a view-invariant paradigm, aligning shared features across views to achieve robustness. However, view-invariant inherently enforces part-level alignment, which ignores view-specific cues and discriminative identity information. To this end, this work proposes ViSA (View-aware Semantic Alignment), a view-aware framework that achieves cross-view semantic consistency containing an Expert-driven Token Generation Module (ETGM) and a Dual-branch Local Fusion Module (DLFM). Technically, the former constructs a set of view-aware experts to generate adaptive semantic queries that perceive viewpoint-specific patterns, while the latter leverages graph reasoning to extract and align local regions responsive to different experts. Extensive experiments on three AGPReID benchmarks including AG-ReID.v2, CARGO and LAGPeR demonstrate that ViSA consistently achieves superior performance, with a notable 10.06% mAP improvement on the challenging CARGO cross-view protocol. The code is available at https://github.com/Cat-Zero/ViSA.
Quan Zhang, Zeqiang Cai, Peiming Zhao +4
Sun Yat-sen University, China · Pazhou Lab (HuangPu), Guangdong, China · Guangdong Province Key Laboratory of Information Security Technology, Guangzhou, China +1
Referring detection refers to locate the target referred by natural languages, which has recently attracted growing research interests. However, existing datasets are limited to ground images with large object centered in relative small scenes. This paper introduces a large-scale challenging dataset for referring detection in aerial images, termed as RefAerial. It distinguishes from conventional ground referring detection datasets by 4 characteristics: (1) low but diverse object-to-scene ratios, (2) numerous targets and distractors, (3)complex and fine-grained referring descriptions, (4) diverse and broad scenes in the aerial view. We also develop a human-in-the-loop referring expansion and annotation engine (REA-Engine) for efficient semi-automated referring pair annotation. Besides, we observe that existing ground referring detection approaches exhibiting serious performance degradation on our aerial dataset since the intrinsic scale variety issue within or across aerial images. Therefore, we further propose a novel scale-comprehensive and sensitive (SCS) framework for referring detection in aerial images. It consists of a mixture-of-granularity (MoG) attention and a two-stage comprehensive-to-sensitive (CtS) decoding strategy. Specifically, the mixture-of-granularity attention is developed for scale-comprehensive target understanding. In addition, the two-stage comprehensive-to-sensitive decoding strategy is designed for coarse-to-fine referring target decoding. Eventually, the proposed SCS framework achieves remarkable performance on our aerial referring detection dataset and even promising performance boost on conventional ground referring detection datasets.
Guyue Hu, Hao Song, Yuxing Tong +5
School of Artificial Intelligence, Anhui University · State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, China · Anhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University, Hefei, China +2
Aerial-Ground Person Re-IDentification (AG-ReID) aims to retrieve the same person across heterogeneous aerial and ground camera platforms. Although great progress has been made, existing methods remain suboptimal due to the direct feature alignment across views, overlooking view-specific cues. To address this issue, we propose a novel Hierarchical Hyperbolic Representation (HiHR) framework for AG-ReID. More specifically, we first extract multi-granularity features based on pre-trained visual-text encoders. Then, we propose a Text-guided Multi-granularity Fusion (TMF) to fuse multi-granularity features and enhance the representation ability of identity features. Furthermore, we introduce the Hierarchical Hyperbolic Learning (HHL) to construct a hierarchical feature structure in a hyperbolic space. This hierarchy includes a coarse level that ensures identity separability and cross-view consistency, and a fine level that preserves view-specific discriminative cues. As a result, our proposed framework can effectively aggregate view-invariant and view-specific discriminative features for AG-ReID. Extensive experiments on four AG-ReID benchmarks demonstrate the effectiveness of our framework. The source code is available at https://github.com/YangQiWei3/HiHR.