The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2609.14560v1 Announce Type: new Abstract: General-purpose object detectors lose accuracy on UAV footage, where targets span only a handful of pixels and onboard compute is limited. Prior work c...
arXiv:2604.11508v3 Announce Type: replace Abstract: Fine-tuning a pretrained classifier leaves some samples reliably learned and others cycling between correct and incorrect. Curriculum learning, dat...
General-purpose object detectors lose accuracy on UAV footage, where targets span only a handful of pixels and onboard compute is limited. Prior work composes independently-validated architectural tec...
arXiv:2607. 29592v1 Announce Type: cross Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks.
arXiv:2608. 13061v1 Announce Type: new Abstract: As the feature representation changes, replay must preserve the earlier classes.
The paper investigates the use of evidential deep learning (EDL) for multi‑modal anti‑UAV detection, comparing it with sigmoid baselines, Dempster‑Shafer evidence fusion, and uncertainty‑driven temporal sensor gating across three benchmarks (thermal tracking, RGB‑audio‑RF classification, and RGB‑IR tracking). EDL improves accuracy by up to 5.9 percentage points and better ranks classification errors, while the other components (DS fusion, Dirichlet vacuity, temporal gating) do not provide the expected benefits. The study concludes that the primary advantage of EDL stems from its training objective rather than its uncertainty estimates.