TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer

• URL: http://arxiv.org/abs/2206.06619v1
• Date: Tue, 14 Jun 2022 06:27:38 GMT
• Title: TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer
• Authors: Jiajun Deng, Zhengyuan Yang, Daqing Liu, Tianlang Chen, Wengang Zhou, Yanyong Zhang, Houqiang Li and Wanli Ouyang
• Abstract summary: We explore neat yet effective Transformer-based frameworks for visual grounding. TransVG establishes multi-modal correspondences by Transformers and localizes referred regions by directly regressing box coordinates. We upgrade our framework to a purely Transformer-based one by leveraging Vision Transformer (ViT) for vision feature encoding.
• Score: 188.00681648113223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we explore neat yet effective Transformer-based frameworks for visual grounding. The previous methods generally address the core problem of visual grounding, i.e., multi-modal fusion and reasoning, with manually-designed mechanisms. Such heuristic designs are not only complicated but also make models easily overfit specific data distributions. To avoid this, we first propose TransVG, which establishes multi-modal correspondences by Transformers and localizes referred regions by directly regressing box coordinates. We empirically show that complicated fusion modules can be replaced by a simple stack of Transformer encoder layers with higher performance. However, the core fusion Transformer in TransVG is stand-alone against uni-modal encoders, and thus should be trained from scratch on limited visual grounding data, which makes it hard to be optimized and leads to sub-optimal performance. To this end, we further introduce TransVG++ to make two-fold improvements. For one thing, we upgrade our framework to a purely Transformer-based one by leveraging Vision Transformer (ViT) for vision feature encoding. For another, we devise Language Conditioned Vision Transformer that removes external fusion modules and reuses the uni-modal ViT for vision-language fusion at the intermediate layers. We conduct extensive experiments on five prevalent datasets, and report a series of state-of-the-art records.

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