Chang Liu

Aoting Zhang, Dongbao Yang, Chang Liu et al.

Adapting Detection Transformers to Incremental Object Detection (IOD) poses a systemic challenge, as set-based optimization is inherently destabilized by sequential learning. In this work, we identify Gradient Dilution as the root cause of performance degradation, wherein optimization signals required to preserve old knowledge are progressively weakened. This phenomenon manifests as a cascading erosion of preservation gradients in magnitude, direction, and support coverage, driven by three tightly coupled factors: Signal Dispersion, where foreground gradients are overwhelmed by background noise; Assignment Drift, where stochastic query-target matching induces inconsistent gradient trajectories; and Support Attrition, where gradients from retained samples insufficiently cover the old-class feature space, weakening decision boundaries under interference from new classes. To counteract this, we propose FAS, a unified framework that Focuses, Aligns, and Sustains gradient flow throughout incremental learning. Specifically, we introduce prior-injected queries to focus discriminative signals by filtering background interference at the source. We further propose deterministic anchor distillation to align query-target assignments and enforce semantic consistency across stages under unstable matching. Finally, we devise manifold-support replay to sustain distributional support of old classes, counteracting representational erosion induced by continual updates. Extensive experiments show that FAS restores robust optimization dynamics and outperforms state-of-the-art methods, achieving over 5.0 AP improvement in the challenging 40+10x4 incremental setting.

Nguyen Duc Minh Quang, Ruoxi Chong, Zhiqiang Wei et al.

The Low-Altitude Economy (LAE) is rapidly expanding, giving rise to low-altitude wireless networks (LAWNs), where large-scale cellular-connected unmanned aerial vehicle (UAV) deployments support heterogeneous mission-critical applications over multi-cell ground base station (GBS) infrastructures. To ensure mission success, each UAV must jointly optimize communication throughput and mission completion efficiency. In fifth-generation (5G) new radio (NR) systems, the equal resource block (RB) allocation policy induces strong strategic coupling among UAV trajectories: when a UAV enters a GBS cell, it reduces the RB share available to all co-served UAVs, thereby altering their achievable rates and trajectory incentives through shared wireless resources. Existing studies either ignore this coupling or focus on single-cell infrastructure, leaving the multi-cell, congestion-aware UAV trajectory planning problem insufficiently addressed. To fill this gap, we formulate the problem as a cooperative stochastic congestion game with a communication-and-mission-aware utility function, and propose a centralized-training decentralized-execution multi-agent proximal policy optimization (CTDE-MAPPO) algorithm to maximize social welfare under multi-cell RB congestion. Simulation results show that the proposed method outperforms QMIX, independent Q-learning, and random baselines in terms of aggregate utility and mission success rate, while achieving stable convergence within practical training budgets.