Name
Abstract | ||
|---|---|---|
Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi-modal inputs. In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT), which provides the VLN agent with action prompts to enable the explicit learning of action-level modality alignment to pursue successful navigation. Specifically, an action prompt is defined as a modality-aligned pair of an image sub-prompt and a text sub-prompt, where the former is a single-view observation and the latter is a phrase like “walk past the chair”. When starting navigation, the instruction-related action prompt set is retrieved from a prebuilt action prompt base and passed through a prompt encoder to obtain the prompt feature. Then the prompt feature is concatenated with the original instruction feature and fed to a multilayer transformer for action prediction. To collect high-quality action prompts into the prompt base, we use the Contrastive Language-Image Pretraining (CLIP) model which has powerful cross-modality alignment ability. A modality alignment loss and a sequential consistency loss are further introduced to enhance the alignment of the action prompt and enforce the agent to focus on the related prompt sequentially. Experimental results on both R2R and RxR show the superiority of ADAPT over state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.01496 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Vision + language | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingqian Lin | 1 | 0 | 1.01 |
| Yi Zhu | 2 | 17 | 4.27 |
| Zicong Chen | 3 | 0 | 0.34 |
| Xiaodan Liang | 4 | 1096 | 77.53 |
| Jianzhuang Liu | 5 | 1614 | 98.72 |
| Xiaodan Liang | 6 | 37 | 9.73 |