# Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework

arXiv: https://arxiv.org/abs/2606.18841 · HTML: https://mlsanity.com/paper/2606.18841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 53 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 28 of 28)

- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Conflict-Averse Gradient Descent for Multi-task Learning](https://mlsanity.com/paper/2110.14048/) (2021)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Multi-Task Learning with Multi-Query Transformer for Dense Prediction](https://mlsanity.com/paper/2205.14354/) (2022)
- [DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection](https://mlsanity.com/paper/2312.15742/) (2023)
- [INTERN: A New Learning Paradigm Towards General Vision](https://mlsanity.com/paper/2111.08687/) (2021)
- [Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities](https://mlsanity.com/paper/2401.01544/) (2024)
- [AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification](https://mlsanity.com/paper/2401.02634/) (2024)
- [FedHCA2: Towards Hetero-Client Federated Multi-Task Learning](https://mlsanity.com/paper/2311.13250/) (2023)
- [Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark](https://mlsanity.com/paper/2503.06983/) (2025)
- [A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective](https://mlsanity.com/paper/2309.13989/) (2023)
- [AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification](https://mlsanity.com/paper/2503.08121/) (2025)
- [AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios](https://mlsanity.com/paper/2506.16371/) (2025)
- [Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation](https://mlsanity.com/paper/2009.13342/) (2020)
- [Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition](https://mlsanity.com/paper/2507.21610/) (2025)
- [Decoding with Structured Awareness: Integrating Directional, Frequency-Spatial, and Structural Attention for Medical Image Segmentation](https://mlsanity.com/paper/2512.05494/) (2025)
- [TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning](https://mlsanity.com/paper/2501.04293/) (2025)
- [MambaSeg: Harnessing Mamba for Accurate and Efficient Image-Event Semantic Segmentation](https://mlsanity.com/paper/2512.24243/) (2025)
- [SEMC: Structure-Enhanced Mixture-of-Experts Contrastive Learning for Ultrasound Standard Plane Recognition](https://mlsanity.com/paper/2511.12559/) (2025)
- [Bidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation](https://mlsanity.com/paper/2601.05855/) (2026)
- [Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric](https://mlsanity.com/paper/2305.15764/) (2023)
- [One Framework to Rule Them All: Unifying Multimodal Tasks with LLM Neural-Tuning](https://mlsanity.com/paper/2408.03001/) (2024)
- [A2VISR: An Active and Adaptive Ground-Aerial Localization System Using Visual Inertial and Single-Range Fusion](https://mlsanity.com/paper/2512.16367/) (2025)

## Abstract (arXiv)

Air-ground collaborative perception is crucial for robust visual understanding in real-world dynamic environments. However, existing studies typically formulate collaboration as single-task cross-view fusion, overlooking the functional dependencies among localization, target association, and fine-grained parsing. In addition, the heterogeneous nature of aerial and ground views introduces substantial geometric, scale, and occlusion discrepancies, making uniform feature sharing vulnerable to negative transfer. To tackle these issues, we model air-ground perception as a progressive cross-task collaboration task and construct the Air-Ground Progressive Collaboration (AGPC) benchmark, a spatio-temporally aligned benchmark comprising more than 745K raw video frames. Built upon this benchmark, we propose Socialized Co-Perception (SCP), a coarse-to-fine framework that organizes collaboration progressively from aerial global localization to ground target association and identity-aware parsing. Its core module, the Dual-Layer Router (DLR), decouples input-side multi-scale expert selection from output-side task-conditioned modulation, enabling selective cross-view and cross-task interaction while suppressing harmful interference. Extensive experiments demonstrate the effectiveness of SCP. It achieves a 3.73% coevolutionary gain and a 7.86% improvement in average downstream performance. These results show that task-conditioned collaboration is more effective than uniform fusion for heterogeneous air-ground perception. The code is available at https://github.com/g1136639260-spec/AGSCP.