MM-Conv: A Multimodal Dataset and Benchmark for Context-Aware Grounding in 3D Dialogue

2024-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

MM-Conv introduces a multimodal dataset and benchmark designed to improve context-aware language grounding in dynamic 3D dialogue. This resource comprises 6.7 hours of egocentric VR interaction data, synchronizing speech, motion, gaze, and 3D scene geometry. It includes over 4,200 manually verified referring expressions, categorized into full, partitive, and pronominal types. The benchmark evaluates a two-stage grounding pipeline that first resolves conversational ambiguity through contextual rewriting before visual localization. This approach significantly boosts grounding performance by 11–22 percentage points on average. For instance, GroundingDINO achieved 56.7% accuracy on pronominal references after rewriting, nearly doubling end-to-end baselines. The findings highlight that decoupling linguistic reasoning from visual perception is more effective for conversational grounding than monolithic end-to-end VLMs, which struggle with spontaneous, ambiguous language.

Key takeaway

For AI Scientists and Machine Learning Engineers developing embodied AI or conversational agents, you should prioritize modular architectures that separate linguistic disambiguation from visual grounding. Simply providing dialogue history to end-to-end VLMs is insufficient for resolving ambiguous references like pronouns. Instead, implement a contextual rewriting stage to explicitly clarify expressions before visual localization, as this strategy demonstrably improves grounding performance by 11–22 percentage points, enabling more robust and human-like interaction in dynamic 3D environments.

Key insights

Decoupling linguistic disambiguation from visual localization significantly improves multimodal grounding in dynamic dialogue.

Principles

• Spontaneous dialogue requires context-aware linguistic reasoning for referential ambiguity.
• Modular architectures outperform end-to-end VLMs for situated grounding.
• Non-verbal cues like gaze and gesture are crucial for human referential understanding.

Method

A two-stage pipeline first uses Qwen2.5-VL for text-only contextual rewriting of ambiguous expressions into explicit descriptions, then passes these to a visual grounding model (e.g., GroundingDINO) for localization.

In practice

• Implement a pre-processing step to rewrite ambiguous conversational references.
• Utilize egocentric VR data for training models on dynamic, multimodal interactions.

Topics

• Multimodal Grounding
• Vision-Language Models
• Referential Communication
• 3D Dialogue Datasets
• Contextual Rewriting
• Embodied AI

Code references

• MahmoudAshraf97/ctc-forced-aligner
• m-bain/whisperX
• lzw-lzw/GroundingGPT
• microsoft/Florence-2
• allenai/ai2thor

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.