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The paper presents a two‑stage approach to street‑panorama semantic segmentation using Vision Transformer ensembles. In the first stage, nine pretrained models are compared under equal compute budgets, and the best two are trained with three random seeds, achieving 60.95% mIoU on validation and 57.08% on the hidden test leaderboard. The second stage explores augmentations, larger inputs, and additional architectures, culminating in an ensemble of four models that scores 64.53% mIoU on validation and 61.54% mIoU (71.93% F1) on the hidden test leaderboard, while the study documents all runs, costs, and releases the code and models.
arXiv:2609.01584v1 Announce Type: new Abstract: Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traf...
The paper introduces a lightweight full-frame detector for partially manipulated AI-generated videos, suitable for edge deployment without face-detection preprocessing. It distills a DINOv2-Base teacher into a frozen MobileNetV3-Small student using temperature-annealed soft-label transfer, attention-diversity regularization, frame-level supervision, and a residual feature adapter. The model addresses false positives on legitimate scene cuts and threshold-level miscalibration, achieving an AUC of 0.766 on a 55,393-sample spliced test set while running at 3.65 ms per 16‑frame clip with a 150.4 MB checkpoint.
arXiv:2607. 13234v1 Announce Type: cross Abstract: Deepfake detectors that achieve near-perfect scores on academic benchmarks collapse on real-world content: recent in-the-wild evaluations report AUC drops of 45-50% for state-of-the-art open-source models.
arXiv:2606. 17615v1 Announce Type: cross Abstract: Estimating human proficiency from video is a key challenge for automated skill assessment, with applications in sports coaching, music pedagogy, surgical training, and workplace learning.
PairCLIP‑SWA is a single‑backbone deepfake detector that fine‑tunes an OpenCLIP ViT‑L/14 visual tower using a class‑balanced corpus created by CLIP‑similarity‑guided subsampling and then applies stochastic weight averaging over the fine‑tuning trajectory. Trained on the IEEE SP Cup 2025 (DF‑Wild‑Cup) benchmark, the weight‑averaged model achieves 97.04 % accuracy, 98.64 % AU‑ROC and a 4.07 % equal error rate—outperforming the best published three‑backbone ensemble. The study shows that averaging stabilizes validation performance, prevents misranking on disjoint test sets, and that CLIP pre‑training, rather than ensembling, is the key driver of accuracy.