Grounded Entity-Landmark Adaptive Pre-training for Vision-and-Language Navigation
This addresses a critical but seldom-considered problem in VLN for improving navigation accuracy, though it is incremental as it builds on existing pre-training methods with new annotations and objectives.
The paper tackles the problem of fine-grained cross-modal alignment at the entity level in Vision-and-Language Navigation (VLN) by proposing a Grounded Entity-Landmark Adaptive (GELA) pre-training paradigm, which achieves state-of-the-art results on R2R and CVDN benchmarks.
Cross-modal alignment is one key challenge for Vision-and-Language Navigation (VLN). Most existing studies concentrate on mapping the global instruction or single sub-instruction to the corresponding trajectory. However, another critical problem of achieving fine-grained alignment at the entity level is seldom considered. To address this problem, we propose a novel Grounded Entity-Landmark Adaptive (GELA) pre-training paradigm for VLN tasks. To achieve the adaptive pre-training paradigm, we first introduce grounded entity-landmark human annotations into the Room-to-Room (R2R) dataset, named GEL-R2R. Additionally, we adopt three grounded entity-landmark adaptive pre-training objectives: 1) entity phrase prediction, 2) landmark bounding box prediction, and 3) entity-landmark semantic alignment, which explicitly supervise the learning of fine-grained cross-modal alignment between entity phrases and environment landmarks. Finally, we validate our model on two downstream benchmarks: VLN with descriptive instructions (R2R) and dialogue instructions (CVDN). The comprehensive experiments show that our GELA model achieves state-of-the-art results on both tasks, demonstrating its effectiveness and generalizability.