  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.
