arXiv Machine Learning August 13, 2026 By Vaishnav Raju

Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines

Read the original on arXiv Machine Learning →

arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems

arXiv:2607. 22714v1 Announce Type: cross Abstract: Real-time perception is a foundational requirement for advanced driver assistance systems (ADAS) and autonomous vehicles, yet embedded automotive platforms impose severe constraints on compute, memory, and power.

By Sai Sidharth D

agents computer-vision efficiency

arXiv AI

Jun 3

Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.

By Glenn Jocher, Jing Qiu, Mengyu Liu, Shuai Lyu, Fatih Cagatay Akyon, Muhammet Esat Kalfaoglu

llms computer-vision

arXiv AI

Jun 17

Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones

arXiv:2603. 26551v2 Announce Type: replace-cross Abstract: Vision backbone networks play a central role in modern computer vision.

By Moritz Nottebaum, Matteo Dunnhofer, Christian Micheloni

computer-vision benchmarks

arXiv AI

Jul 14

Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles

arXiv:2607. 10942v1 Announce Type: cross Abstract: Physical AI systems, such as autonomous vehicles and intelligent machines, require transformer-based perception models that satisfy stringent edge latency and energy constraints.

By Ashiyana Abdul Majeed, Mahmoud Meribout, Neethu Joseph, Abel Kidane Haile, Mohammad Abdullah Al Faruque

llms agents computer-vision efficiency

arXiv AI

Jul 9

MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices

arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.

By Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, M. Hassan Najafi

efficiency

arXiv Machine Learning

Jul 22

Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics

arXiv:2607. 18540v1 Announce Type: cross Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-time inference.

By Hamidreza Yaghoubi Araghi, Parastoo Pilevar, Ming C. Lin

llms robotics fine-tuning efficiency benchmarks

Related stories

Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems

Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones

Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles

MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices

Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics