VGent: Visual Grounding via Modular Design for Disentangling Reasoning and Prediction

TL;DR

VGent introduces a modular visual grounding model that disentangles reasoning and prediction, leveraging a frozen MLLM and detector-based queries for fast, accurate multi-target grounding, achieving state-of-the-art results.

Contribution

The paper presents VGent, a novel modular encoder-decoder architecture that separates reasoning from prediction, enabling fast inference and modular upgrades for improved visual grounding performance.

Findings

Achieves +20.6% F1 improvement over prior methods.

Boosts gIoU by +8.2% and cIoU by +5.8%.

Maintains constant, fast inference latency.

Abstract

Current visual grounding models are either based on a Multimodal Large Language Model (MLLM) that performs auto-regressive decoding, which is slow and risks hallucinations, or on re-aligning an LLM with vision features to learn new special or object tokens for grounding, which may undermine the LLM's pretrained reasoning ability. In contrast, we propose VGent, a modular encoder-decoder architecture that explicitly disentangles high-level reasoning and low-level bounding box prediction. Specifically, a frozen MLLM serves as the encoder to provide untouched powerful reasoning capabilities, while a decoder takes high-quality boxes proposed by detectors as queries and selects target box(es) via cross-attending on encoder's hidden states. This design fully leverages advances in both object detection and MLLM, avoids the pitfalls of auto-regressive decoding, and enables fast inference. Moreover, it supports modular upgrades of both the encoder and decoder to benefit the whole system: we introduce (i) QuadThinker, an RL-based training paradigm for enhancing multi-target reasoning ability of the encoder; (ii) mask-aware label for resolving detection-segmentation ambiguity; and (iii) global target recognition to improve the recognition of all the targets which benefits the selection among augmented proposals. Experiments on multi-target visual grounding benchmarks show that VGent achieves a new state-of-the-art with +20.6% F1 improvement over prior methods, and further boosts gIoU by +8.2% and cIoU by +5.8% under visual reference challenges, while maintaining constant, fast inference latency.