2 papers
cs.LG2026
OneComp: One-Line Revolution for Generative AI Model Compression
Yuma Ichikawa, Keiji Kimura, Akihiro Yoshida +11
Deploying foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can mitigate these bottlenecks by reducing the p…
cs.CV2025
Enhancing Quantum-ready QUBO-based Suppression for Object Detection with Appearance and Confidence Features
Keiichiro Yamamura, Toru Mitsutake, Hiroki Ishikura +3
Quadratic Unconstrained Binary Optimization (QUBO)-based suppression in object detection is known to have superiority to conventional Non-Maximum Suppression (NMS), especially for…