arXiv:2506.01850v2 Announce Type: replace-cross 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 m
The rapid advancement and widespread adoption of MLLMs for instruction-following tasks necessitate solutions for current limitations in fine-grained visual understanding.
Improving MLLMs' ability to process visual details accurately is crucial for their application in complex, instruction-based tasks across various industries.
The explicit addressing of semantic entanglement in visual representations allows MLLMs to interpret visual instructions more precisely, moving towards more capable AI agents.
- · AI developers
- · Robotics
- · Computer vision researchers
- · Industries relying on visual instruction-following
- · Models reliant on broad visual representations
Enhanced MLLM performance in tasks requiring precise visual grounding.
Accelerated development of more sophisticated AI agents capable of nuanced environmental interaction.
Potential for new applications in highly detailed visual inspection, augmented reality, and intuitive human-robot interfaces.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG