SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding

TL;DR

SegVG introduces a novel approach that leverages pixel-level segmentation signals transferred from bounding box annotations to improve visual grounding accuracy, utilizing a multi-task encoder-decoder and domain alignment techniques.

Contribution

The paper proposes a new method that transfers box annotations into segmentation signals and employs a triple alignment module to enhance visual grounding performance.

Findings

Achieves state-of-the-art results on five datasets.

Effectively combines regression and segmentation for grounding.

Mitigates domain discrepancy with triple attention mechanism.

Abstract

Different from Object Detection, Visual Grounding deals with detecting a bounding box for each text-image pair. This one box for each text-image data provides sparse supervision signals. Although previous works achieve impressive results, their passive utilization of annotation, i.e. the sole use of the box annotation as regression ground truth, results in a suboptimal performance. In this paper, we present SegVG, a novel method transfers the box-level annotation as Segmentation signals to provide an additional pixel-level supervision for Visual Grounding. Specifically, we propose the Multi-layer Multi-task Encoder-Decoder as the target grounding stage, where we learn a regression query and multiple segmentation queries to ground the target by regression and segmentation of the box in each decoding layer, respectively. This approach allows us to iteratively exploit the annotation as signals for both box-level regression and pixel-level segmentation. Moreover, as the backbones are typically initialized by pretrained parameters learned from unimodal tasks and the queries for both regression and segmentation are static learnable embeddings, a domain discrepancy remains among these three types of features, which impairs subsequent target grounding. To mitigate this discrepancy, we introduce the Triple Alignment module, where the query, text, and vision tokens are triangularly updated to share the same space by triple attention mechanism. Extensive experiments on five widely used datasets validate our state-of-the-art (SOTA) performance.