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Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation
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This study presents a novel evaluation framework for the Vision-Language Navigation (VLN) task. It aims to diagnose current models for various instruction categories at a finer-grained level. The framework is structured around the context-free grammar (CFG) of the task. The CFG serves as the basis for the problem decomposition and the core premise of the instruction categories design. We propose a semi-automatic method for CFG construction with the help of Large-Language Models (LLMs). Then, we induct and generate data spanning five principal instruction categories (i.e. direction change, landmark recognition, region recognition, vertical movement, and numerical comprehension). Our analysis of different models reveals notable performance discrepancies and recurrent issues. The stagnation of numerical comprehension, heavy selective biases over directional concepts, and other interesting findings contribute to the development of future language-guided navigation systems.
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Cited by 1 Pith paper
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NavBench: Probing Multimodal Large Language Models for Embodied Navigation
NavBench introduces a two-part benchmark for zero-shot embodied navigation evaluation, showing that MLLMs' navigation comprehension correlates with execution and that temporal progress tracking is a major bottleneck.
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