CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
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DroneScan-YOLO reaches 55.3% mAP@50 and 35.6% mAP@50-95 on VisDrone2019-DET by combining 1280x1280 input, RPA-Block pruning, MSFD stride-4 branch, and SAL-NWD loss, beating YOLOv8s by 16.6 and 12.3 points with only 4.1% more parameters.
CODO automates comprehensive dataflow optimization on FPGAs, achieving 1.45x-4.52x speedups on kernels and up to 33.8x on DNN models over state-of-the-art frameworks.
VLM-based harmonization of inconsistent annotations across two document layout corpora raises detection F-score from 0.860 to 0.883 and table TEDS from 0.750 to 0.814 while tightening embedding clusters.
Entropy-gradient grounding uses model uncertainty to retrieve evidence regions in VLMs, improving performance on detail-critical and compositional tasks across multiple architectures.
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
A pipeline uses OpenPose and Gaze-LLE to extract pose and gaze data from classroom videos, deletes the raw footage, and applies an LLM for zero-shot behavioral analysis of student attention.
Logistic regression on frozen DINOv3 features achieves 88.5% macro F1 on the AQUA20 marine species benchmark, matching end-to-end supervised models with only 6% of the labels.
Cello Evaluator is a real-time postural feedback system for cellists running on current Android phones via on-device computer vision, validated as user-friendly by experts.
A system combining object detection, segmentation, keypoint prediction, and homography transforms soccer video into real-world player positions and tactical statistics.
citing papers explorer
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A global dataset of continuous urban dashcam driving
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
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Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings
Logistic regression on frozen DINOv3 features achieves 88.5% macro F1 on the AQUA20 marine species benchmark, matching end-to-end supervised models with only 6% of the labels.