Visual Grounding with Attention-Driven Constraint Balancing

TL;DR

This paper introduces AttBalance, a novel framework that enhances visual grounding by optimizing transformer attention mechanisms to better focus on language-relevant regions, leading to improved accuracy across multiple models and benchmarks.

Contribution

The paper proposes a new Attention-Driven Constraint Balancing framework that improves transformer-based visual grounding models by better aligning visual features with language expressions.

Findings

Achieves consistent improvements over five models on four benchmarks.

Attains new state-of-the-art performance with QRNet.

Enhances attention mechanisms to better focus on relevant regions.

Abstract

Unlike Object Detection, Visual Grounding task necessitates the detection of an object described by complex free-form language. To simultaneously model such complex semantic and visual representations, recent state-of-the-art studies adopt transformer-based models to fuse features from both modalities, further introducing various modules that modulate visual features to align with the language expressions and eliminate the irrelevant redundant information. However, their loss function, still adopting common Object Detection losses, solely governs the bounding box regression output, failing to fully optimize for the above objectives. To tackle this problem, in this paper, we first analyze the attention mechanisms of transformer-based models. Building upon this, we further propose a novel framework named Attention-Driven Constraint Balancing (AttBalance) to optimize the behavior of visual features within language-relevant regions. Extensive experimental results show that our method brings impressive improvements. Specifically, we achieve constant improvements over five different models evaluated on four different benchmarks. Moreover, we attain a new state-of-the-art performance by integrating our method into QRNet.