Referring Transformer: A One-step Approach to Multi-task Visual Grounding
This addresses the need for efficient and high-performance visual reasoning in AI, though it is incremental as it builds on existing transformer architectures.
The paper tackles the problem of visual grounding tasks like referring expression comprehension and segmentation by proposing a simple one-stage multi-task transformer framework, which outperforms state-of-the-art methods by a large margin on both tasks.
As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or require the designing of complex task-specific one-stage architectures. In this paper, we propose a simple one-stage multi-task framework for visual grounding tasks. Specifically, we leverage a transformer architecture, where two modalities are fused in a visual-lingual encoder. In the decoder, the model learns to generate contextualized lingual queries which are then decoded and used to directly regress the bounding box and produce a segmentation mask for the corresponding referred regions. With this simple but highly contextualized model, we outperform state-of-the-arts methods by a large margin on both REC and RES tasks. We also show that a simple pre-training schedule (on an external dataset) further improves the performance. Extensive experiments and ablations illustrate that our model benefits greatly from contextualized information and multi-task training.