Visual Grounding with Transformers
This addresses the problem of accurately locating objects in images based on textual descriptions for computer vision applications, representing a novel method rather than an incremental improvement.
The paper tackles visual grounding by proposing a transformer-based approach that learns semantic-discriminative visual features guided by textual descriptions, outperforming state-of-the-art proposal-free methods on five benchmarks with fast inference speed.
In this paper, we propose a transformer based approach for visual grounding. Unlike previous proposal-and-rank frameworks that rely heavily on pretrained object detectors or proposal-free frameworks that upgrade an off-the-shelf one-stage detector by fusing textual embeddings, our approach is built on top of a transformer encoder-decoder and is independent of any pretrained detectors or word embedding models. Termed VGTR -- Visual Grounding with TRansformers, our approach is designed to learn semantic-discriminative visual features under the guidance of the textual description without harming their location ability. This information flow enables our VGTR to have a strong capability in capturing context-level semantics of both vision and language modalities, rendering us to aggregate accurate visual clues implied by the description to locate the interested object instance. Experiments show that our method outperforms state-of-the-art proposal-free approaches by a considerable margin on five benchmarks while maintaining fast inference speed.