Title: MoDA: Modulation Adapter for Fine-Grained Visual Understanding in Instructional MLLMs
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: MLLM, Visual Instruction, Modulation, Multimodal, LLM, Transformer

Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in instruction-following tasks by integrating pretrained visual encoders with large language models (LLMs). However, existing approaches often struggle with fine-grained visual grounding due to semantic entanglement in visual patch representations, where individual patches blend multiple distinct visual elements, making it difficult for models to focus on instruction-relevant details. To address this challenge, we propose MoDA (Modulation Adapter), a lightweight module that enhances visual grounding through instruction-guided channel-wise modulation. Following the standard LLaVA training protocol, MoDA operates in the second stage by applying cross-attention between language instructions and pre-aligned visual features, generating dynamic modulation masks that emphasize semantically relevant embedding dimensions while de-emphasizing irrelevant information. This targeted refinement enables more precise visual-language alignment without architectural modifications or additional supervision. We conduct comprehensive evaluation across 13 diverse benchmarks spanning visual question answering, vision-centric reasoning, and hallucination detection. MoDA demonstrates substantial improvements, achieving notable gains of +12.0 points on MMVP hallucination detection and +4.8 points on ScienceQA reasoning, while consistently outperforming baselines on 12 out of 13 benchmarks with minimal computational overhead (<1% FLOPs). Our results establish MoDA as an effective, general-purpose enhancement for improving fine-grained visual grounding in instruction-tuned MLLMs.

TL;DR: MoDA is a lightweight adapter that enhances visual grounding in MLLMs, improving accuracy on 12 of 13 benchmarks with minimal overhead.