Among other drawbacks, models trained via federated learning (FL) often struggle to generalize to new, unseen clients – a challenge that becomes more severe in low-data regimes. Most existing solutions address this issue using hypernetworks, which do not scale well with the size of the underlying model. To overcome these limitations, we introduce Federated Personalized Client Embeddings (FedPCE) – a method that uses embeddings to distill personalized knowledge from existing FL approaches. Our results show that FedPCE performs comparably to popular FL algorithms during both training and personalization. Notably, it outperforms competing methods when only limited data is available for personalization—even with as few as 25 labeled samples (Code available at https://github.com/somcogo/fedpce ).
Graph-based deep learning provides an efficient, single-stage solution for reconstructing digital city models from Very-High Resolution (VHR) aerial or satellite imagery. However, these models are highly sensitive to label quality—particularly in detecting roof corners and identifying the relationships between points to form polygons.—making them vulnerable to the misaligned annotations often found in open-source datasets. This study investigates the impact of annotation quality on reconstruction performance and proposes a mitigation strategy using generative synthetic datasets. Leveraging Stable Diffusion with ControlNet, we generate synthetic Red Green Blue (RGB)–label pairs conditioned on misaligned label masks. This process realigns the imagery to noisy annotations, producing spatially consistent training data. Texture control is introduced to enhance visual fidelity and support better feature learning. We evaluate this approach using PolyRoof, a graph-based reconstruction model. While models trained solely on synthetic data achieve moderate performance, pretraining with synthetic datasets followed by fine-tuning with accurately labeled real data leads to significant gains. When integrated into the training pipeline, synthetic pretraining improves reconstruction quality and can even outperform models trained exclusively on real labels. These improvements are observed across both quantitative metrics and qualitative visual results. Overall, the findings indicate that synthetic data—when spatially aligned with noisy annotations and combined with transfer learning—serves as an effective pretraining resource. This can be followed by fine-tuning on real datasets as part of a dataset enrichment strategy aimed at addressing label quality issues in graph-based building reconstruction.
Road traffic accidents claim millions of lives annually, majorly involving vulnerable road users such as pedestrians and cyclists. Proactive traffic scene risk assessment with priority to vulnerable traffic actors can drastically reduce the chances of collision and save lives. To improve the safety of advanced driver-assistance systems (ADAS), we propose Object Risk Estimation (ORE), a novel framework that combines motion, lane-position, and class labels to enable context-aware and ethically prioritized risk assessment. It uses a multitask framework that integrates object detection, Time-to-Contact (TTC) estimation, and semantic segmentation in a single deep neural network. Further, it uses post-processing to combine these outputs to estimate risk per object and overall scene risk. Leveraging a shared backbone, ORE achieves a 27% reduction in inference time while delivering superior performance, with 1.5 higher mAP % and 11% lower oMiD loss on the Waymo dataset. Its modular design ensures transparency, with intermediate outputs directly explaining risk scores, eliminating the need for post-hoc analysis. By facilitating timely transfer of control and protecting vulnerable road users, ORE offers a practical, interpretable, and efficient solution for ADAS and autonomous driving safety, scalable to diverse traffic scenarios and higher autonomy levels.
Accurate cell tracking in microscopy is essential for studying biological dynamics like proliferation and migration. Traditional fully supervised methods demand dense pixel-wise masks for every frame, making them impractical for large-scale use. Recent methods like SAT reduce annotation effort by using sparse point-based supervision, but still require multiple positive and negative points per cell, which remains labor-intensive. BoxTrack offers a lightweight and annotation-efficient alternative, requiring only a single bounding box per cell in the first frame. Without relying on any point-level annotations, it performs end-to-end instance segmentation and tracking over entire sequences. This simplification leads to a substantial reduction in annotation cost while improving performance over SAT. On the CTMC dataset, BoxTrack improves Multiple Object Tracking Accuracy (MOTA) by +15.96 https://github.com/nabeelkhalid92/Box-it-Track-it .
3D occupancy prediction is gaining traction in autonomous driving for its ability to jointly model environment geometry and semantics. Weakly supervised methods learn 3D representations solely from multi-view 2D labels, making them ideal for data-scarce scenarios. However, distilling 2D knowledge into 3D is challenging due to limited information and noisy pseudo labels. To tackle the above challenges, we introduce MT-Occ, a single-view self-supervised 3D occupancy prediction method that enhances 2D-to-3D distillation by leveraging pretrained multi-task features and modelling task interactions. Our approach includes three effective and flexible components: 1) an effective fusion technique utilising pretrained features of multiple relevant tasks, 2) a spatial cross-task attention module for geometric-semantic distillation and 3) a view-consistent label refinement strategy to improve 2D pseudo labels. MT-Occ achieves state-of-the-art results on autonomous driving benchmarks, outperforming prior work with +16.04
