Weakly supervised referring expression grounding (WREG) is an attractive and challenging task for grounding target regions in images by understanding given referring expressions. WREG learns to ground target objects without the manual annotations between image regions and referring expressions during the model training phase. Different from the predominant grounding pattern of existing models, which locates target objects by reconstructing the region-expression correspondence, we investigate WREG from a novel perspective and enrich the prevailing pattern with self-knowledge distillation. Specifically, we propose a target-guided self-knowledge distillation approach that adopts the target prediction knowledge learned from the previous training iterations as the teacher to guide the subsequent training procedure. In order to avoid the misleading caused by the teacher knowledge with low prediction confidence, we present an uncertaintyaware knowledge refinement strategy to adaptively rectify the teacher knowledge by learning dynamic threshold values based on the model prediction uncertainty. To validate the proposed approach, we implement extensive experiments on three benchmark datasets, i.e., Ref Coco, RefCOCO+, and RefCOCOg. Our approach achieves new state-of-the-art results on several splits of the benchmark datasets, showcasing the advantage of the proposed framework for WREG. The implementation codes and trained models are available at: https://github.com/dami23IWREG.sar_KD.
Weakly supervised referring expression grounding (WREG) is an attractive and challenging task for grounding target regions in images by understanding given referring expressions. WREG learns to ground target objects without the manual annotations between image regions and referring expressions during the model training phase. Different from the predominant grounding pattern of existing models, which locates target objects by reconstructing the region-expression correspondence, we investigate WREG from a novel perspective and enrich the prevailing pattern with self-knowledge distillation. Specifically, we propose a target-guided self-knowledge distillation approach that adopts the target prediction knowledge learned from the previous training iterations as the teacher to guide the subsequent training procedure. In order to avoid the misleading caused by the teacher knowledge with low prediction confidence, we present an uncertaintyaware knowledge refinement strategy to adaptively rectify the teacher knowledge by learning dynamic threshold values based on the model prediction uncertainty. To validate the proposed approach, we implement extensive experiments on three benchmark datasets, i.e., Ref Coco, RefCOCO+, and RefCOCOg. Our approach achieves new state-of-the-art results on several splits of the benchmark datasets, showcasing the advantage of the proposed framework for WREG. The implementation codes and trained models are available at: https://github.com/dami23IWREG.sar_KD.