The Role of Entropy in Visual Grounding: Analysis and Optimization

TL;DR

This paper investigates the role of entropy in visual grounding tasks, analyzes its characteristics, and introduces an entropy control algorithm, ECVGPO, to improve model performance across benchmarks.

Contribution

It provides the first detailed analysis of entropy in visual grounding and proposes an interpretable entropy regulation method, ECVGPO, for enhanced model optimization.

Findings

ECVGPO improves performance across multiple benchmarks.

Entropy control balances exploration and exploitation effectively.

Analysis reveals unique entropy characteristics in visual grounding.

Abstract

Recent advances in fine-tuning multimodal large language models (MLLMs) using reinforcement learning have achieved remarkable progress, particularly with the introduction of various entropy control techniques. However, the role and characteristics of entropy in perception-oriented tasks like visual grounding, as well as effective strategies for controlling it, remain largely unexplored. To address this issue, we focus on the visual grounding task and analyze the role and characteristics of entropy in comparison to reasoning tasks. Building on these findings, we introduce ECVGPO (Entropy Control Visual Grounding Policy Optimization), an interpretable algorithm designed for effective entropy regulation. Through entropy control, the trade-off between exploration and exploitation is better balanced. Experiments show that ECVGPO achieves broad improvements across various benchmarks and models.