One-stage Referring Expression Comprehension (REC) is a task that requires accurate alignment between text descriptions and visual content. In recent years, numerous efforts have been devoted to cross-modal learning for REC, while the influence of other factors in this task still lacks a systematic study. To fill this gap, we conduct an empirical study in this article. Concretely, we ablate 42 candidate designs/settings based on a common REC framework, and these candidates cover the entire process of one-stage REC from network design to model training. Afterwards, we conduct over 100 experimental trials on three REC benchmark datasets. The extensive experimental results reveal the key factors that affect REC performance in addition to multi-modal fusion, e.g., multi-scale features and data augmentation. Based on these findings, we further propose a simple yet strong model called SimREC, which achieves new state-of-the-art performance on these benchmarks. In addition to these progresses, we also find that with much less training overhead and parameters, SimREC can achieve better performance than a set of large-scale pre-trained models, e.g., UNITER and VILLA, portraying the special role of REC in existing V&L research.

Referring expression comprehension (REC) often requires a large number of instance-level annotations for fully supervised learning, which are laborious and expensive. In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher. Inspired by the recent progress in computer vision, RefTeacher adopts a teacher-student learning paradigm, where the teacher REC network predicts pseudolabels for optimizing the student one. This paradigm allows REC models to exploit massive unlabeled data based on a small fraction of labeled. In particular, we also identify two key challenges in semi-supervised REC, namely, sparse supervision signals and worse pseudo-label noise. To address these issues, we equip RefTeacher with two novel designs called Attention-based Imitation Learning (AIL) and Adaptive Pseudo-label Weighting (APW). AIL can help the student network imitate the recognition behaviors of the teacher, thereby obtaining sufficient supervision signals. APW can help the model adaptively adjust the contributions of pseudo-labels with varying qualities, thus avoiding confirmation bias. To validate RefTeacher, we conduct extensive experiments on three REC benchmark datasets. Experimental results show that RefTeacher obtains obvious gains over the fully supervised methods. More importantly, using only 10% labeled data, our approach allows the model to achieve near 100% fully supervised performance, e.g., only −2.78% on RefCoco. Project: https://refteacher.github.io/.