arXiv Machine Learning
Sep 4

Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language

The paper introduces set difference captioning for autonomous driving datasets, aiming to generate natural‑language descriptions of differences between two image subsets. It adapts a two‑stage approach to focus on object‑centric patches, allowing attribution of differences to specific objects or categories. A new benchmark, AD‑Diff Bench, is presented to evaluate these methods, especially for sparse, real‑world differences, with open‑weight models to ensure reproducibility.

By Julian Truetsch, Felix Hauser, Christoph Stiller, Frank Bieder
Hugging Face Trending Papers
Sep 3

Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language

The paper introduces set difference captioning for autonomous driving datasets, aiming to generate natural‑language descriptions of differences between two image subsets. It adapts a two‑stage approach to focus on object‑centric patches, enabling attribution of differences to specific objects or categories. A new benchmark, AD‑Diff Bench, is presented to evaluate this method, especially for sparse, real‑world differences, and the authors provide open‑weight models and code for reproducibility.