arXiv Machine Learning
Sep 25

Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge

Albireo is an adaptive, energy‑efficient inference framework for video object detection on edge devices that wraps existing detectors without modification. It uses a 10‑dimensional Kalman filter per active object to decide when to skip detector calls, predicting bounding boxes on skipped frames at near‑zero GPU cost. Evaluated on BDD100K with YOLO and RF‑DETR detectors on NVIDIA Jetson AGX Thor and Orin, Albireo maintains AP@50 within ±1.2 pp of full‑frame inference while reducing energy consumption by 12.1–17.6 % and improving accuracy for some models.

By Amir Taherin, Jos\'e Cano, Bin Ren, Yanzhi Wang, David Kaeli
arXiv Computer Vision
Sep 30

TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining

TT-VidT is a video pretraining method that decouples the temporal axis by combining a per‑frame ViT-B/16 spatial encoder with a compact Temporal Transfer Layer trained via Diff Compression. The authors conduct a systematic 24‑configuration study to isolate architecture, objective, and decoder effects, showing that the full TT-VidT design yields the strongest motion‑sensitive representations. In downstream fine‑tuning, TT‑VidT outperforms state‑of‑the‑art baselines on Jester, Something‑Something V2, ARID, and Diving48 while using significantly fewer encoder FLOPs.

By Shih-Ying Yeh, Daniel Z. Kaplan, Xuehai Wang, Fu-En Yang, Min-Hung Chen, Shang-Hong Lai
arXiv AI
Oct 28

Smart Surveillance: Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking

The paper evaluates event-triggered recording strategies for static surveillance cameras, comparing six motion-based triggers, a CNN detector, strided detection, and hybrid approaches on a benchmark of seven scenes. Detector-based triggers reduce idle frame storage to 19‑28% versus 36‑89% for motion triggers, and remain robust under camera jitter, keeping under 29% idle frames. In addition, the study shows that increasing YOLO11n input resolution improves detection accuracy more than larger models, and that longer track memory boosts tracking IDF1, though detection remains the limiting factor.

By Shahran Rahman Alve
arXiv Computer Vision
Oct 27

Rethinking Video Storage for AI Training: Breaking Cross-Frame Dependencies for Efficient Random Access

arXiv:2610.33079v1 Announce Type: new Abstract: Large-scale AI training increasingly relies on massive video datasets with random temporal sampling patterns. However, existing video storage paradigms...

By Zetian Song, Qize Yu, Yue Chen, Bohan Zhou, Bowen Ping, Yuqi Ye, Haocheng Tang, Haotian Zhang, Qizhe Wang, Shiyu Huang, Jiaqi Zhang, Siwei Ma, Wen Gao
arXiv AI
Sep 28

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

The paper introduces GVCC, a zero‑shot video compression framework that uses a pretrained generative video model as the decoder. GVCC transforms deterministic rectified‑flow samplers into stochastic processes, enabling the transmission of compressed information through per‑step stochastic innovations. The authors evaluate three GVCC variants—Text‑to‑Video, Image‑to‑Video, and First‑Last‑Frame‑to‑Video—on the UVG dataset, reporting perceptual, fidelity, and temporal metrics without claiming global rate‑distortion gains.

By Ziyue Zeng, Xun Su, Haoyuan Liu, Bingyu Lu, Yui Tatsumi, Hiroshi Watanabe