Vision-and-Language Navigation tasks require an agent to navigate to a destination following natural language instructions. We focus on a challenging VLN dataset, Aerial Vision-and-Dialog Navigation, which encompasses a diverse array of environments and includes an additional altitude variable. Significant spatial and scale variations in the aerial agent's view make destination visual grounding a crucial capability for the navigation task. However, the existing frameworks not only have insufficient attention to the vision model, but also lack the correlations between visual and textual modalities. To address this, we propose a model that aligns destination visual images with navigation instructions, featuring three innovative components. Firstly, we propose a multi-stage pre-training pipeline that enhances the model's ability to associate language instructions with top-view images of destinations. Secondly, trajectories are augmented elastically to simulate the noise of the controlling process. Finally, a polygon regression loss function is introduced for rotated object detection, which significantly enhances the accuracy of altitude and orientation estimation. Experiments demonstrate the effectiveness of our approach, which achieves state-of-the-art advancements with improvements of 2.9% in the val unseen dataset and 3.0% in test unseen dataset in success weighted by path length.

Vision-Language Pre-training (VLP) has been shown to be an efficient method to improve the performance of models on different vision-and-language downstream tasks. Substantial studies have shown that neural networks may be able to learn some general rules about language and visual concepts from a large-scale weakly labeled image-text dataset. However, most of the public cross-modal datasets that contain more than 100M image-text pairs are in English; there is a lack of available large-scale and high-quality Chinese VLP datasets. In this work, we propose a new framework for automatic dataset acquisition and cleaning with which we construct a new large-scale and high-quality cross-modal dataset named as TaiSu, containing 166 million images and 219 million Chinese captions. Compared with the recently released Wukong dataset, our dataset is achieved with much stricter restrictions on the semantic correlation of image-text pairs. We also propose to combine texts collected from the web with texts generated by a pre-trained image-captioning model. To the best of our knowledge, TaiSu is currently the largest publicly accessible Chinese cross-modal dataset. Furthermore, we test our dataset on several vision-language downstream tasks. TaiSu outperforms BriVL by a large margin on the zero-shot image-text retrieval task and zero-shot image classification task. TaiSu also shows better performance than Wukong on the image-retrieval task without using image augmentation for training. Results demonstrate that TaiSu can serve as a promising VLP dataset, both for understanding and generative tasks. More information can be referred to https://github.com/ksOAn6g5/TaiSu.