ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way

• URL: http://arxiv.org/abs/2507.08679v2
• Date: Tue, 16 Sep 2025 12:31:06 GMT
• Title: ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
• Authors: Rajarshi Roy, Devleena Das, Ankesh Banerjee, Arjya Bhattacharjee, Kousik Dasgupta, Subarna Tripathi,
• Abstract summary: ByDeWay is a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs)<n>ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP)<n>It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model.
• Score: 8.242020781632801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which improves spatial reasoning and grounding without modifying any model parameters. It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model. These structured, depth-aware captions are appended to the image-question prompt, enriching it with spatial context. This guides MLLMs to produce more grounded and less hallucinated responses. Our method is lightweight, modular, and compatible with black-box MLLMs. Experiments on hallucination-sensitive (POPE) and reasoning-intensive (GQA) benchmarks show consistent improvements across multiple MLLMs, validating the effectiveness of depth-aware prompting in a zero-training setting.

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