VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection

• URL: http://arxiv.org/abs/2206.09111v1
• Date: Sat, 18 Jun 2022 04:08:19 GMT
• Title: VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection
• Authors: Yu Cui, Moshiur Farazi
• Abstract summary: We propose a BERT-like transformer model for Visual Relationship Detection with a multi-stage training strategy. We show that our simple BERT-like model is able to outperform the state-of-the-art VRD models in predicate prediction.
• Score: 0.30458514384586394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Relationship Detection (VRD) impels a computer vision model to 'see' beyond an individual object instance and 'understand' how different objects in a scene are related. The traditional way of VRD is first to detect objects in an image and then separately predict the relationship between the detected object instances. Such a disjoint approach is prone to predict redundant relationship tags (i.e., predicate) between the same object pair with similar semantic meaning, or incorrect ones that have a similar meaning to the ground truth but are semantically incorrect. To remedy this, we propose to jointly train a VRD model with visual object features and semantic relationship features. To this end, we propose VReBERT, a BERT-like transformer model for Visual Relationship Detection with a multi-stage training strategy to jointly process visual and semantic features. We show that our simple BERT-like model is able to outperform the state-of-the-art VRD models in predicate prediction. Furthermore, we show that by using the pre-trained VReBERT model, our model pushes the state-of-the-art zero-shot predicate prediction by a significant margin (+8.49 R@50 and +8.99 R@100).

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