How does negative sampling affect inference efficiency and accuracy tradeoffs across different model scales in

Abstract

Abstract: This paper presents a focused investigation into real-time segmentation in unstructured environments, a crucial aspect for enabling autonomous navigation in off-road robots. To address this challenge, an improved variant of the DDRNet23-slim model is proposed, which includes a lightweight network architecture and reclassifies ten different categories, including drivable roads, trees, high vegetation, obstacles, and buildings, based on the RUGD dataset. The model’s design includes the integration of the semantic-aware normalization and semantic-aware whitening (SAN–SAW) module into the main net

Research Question

How does negative sampling affect inference efficiency and accuracy tradeoffs across different model scales in domain-agnostic question answering?

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