  Semi-Supervised Visual Grounding (SSVG) is a new challenge for its sparse
labeled data with the need for multimodel understanding. A previous study,
RefTeacher, makes the first attempt to tackle this task by adopting the
teacher-student framework to provide pseudo confidence supervision and
attention-based supervision. However, this approach is incompatible with
current state-of-the-art visual grounding models, which follow the
Transformer-based pipeline. These pipelines directly regress results without
region proposals or foreground binary classification, rendering them unsuitable
for fitting in RefTeacher due to the absence of confidence scores. Furthermore,
the geometric difference in teacher and student inputs, stemming from different
data augmentations, induces natural misalignment in attention-based
constraints. To establish a compatible SSVG framework, our paper proposes the
ACTive REtraining approach for Semi-Supervised Visual Grounding, abbreviated as
ACTRESS. Initially, the model is enhanced by incorporating an additional
quantized detection head to expose its detection confidence. Building upon
this, ACTRESS consists of an active sampling strategy and a selective
retraining strategy. The active sampling strategy iteratively selects
high-quality pseudo labels by evaluating three crucial aspects: Faithfulness,
Robustness, and Confidence, optimizing the utilization of unlabeled data. The
selective retraining strategy retrains the model with periodic
re-initialization of specific parameters, facilitating the model's escape from
local minima. Extensive experiments demonstrates our superior performance on
widely-used benchmark datasets.
