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摘要: 视觉语言导航, 即在一个未知环境中, 智能体从一个起始位置出发, 结合指令和周围视觉环境进行分析, 并动态响应生成一系列动作, 最终导航到目标位置. 视觉语言导航有着广泛的应用前景, 该任务近年来在多模态研究领域受到了广泛关注. 不同于视觉问答和图像描述生成等传统多模态任务, 视觉语言导航在多模态融合和推理方面, 更具有挑战性. 然而由于传统模仿学习的缺陷和数据稀缺的现象, 模型面临着泛化能力不足的问题. 系统地回顾了视觉语言导航的研究进展, 首先对于视觉语言导航的数据集和基础模型进行简要介绍; 然后全面地介绍视觉语言导航任务中的代表性模型方法, 包括数据增强、搜索策略、训练方法和动作空间四个方面; 最后根据不同数据集下的实验, 分析比较模型的优势和不足, 并对未来可能的研究方向进行了展望.Abstract: Vision-and-language navigation means that an agent in an unknown environment, starting from a starting location, dynamically generates a series of actions by making analysis with language instructions and the visual environment, and finally navigates to the goal location. And due to the widespread application prospect, in recent years, it has received increasing attention from researchers especially in multi-modal research. It is different from traditional multi-modal tasks such as vision question answer and image captioning, vision-and-language navigation is more challenging in terms of dynamic reasoning and multi-modal fusion. However, with the limitations of imitation learning and the phenomenon of data scarcity, the model is faced with the problem of insufficient generalization. In this paper, we review the current advances in the research of vision-and-language navigation. Firstly, we briefly introduce data sets in visual-and-language navigation. Then, we comprehensively introduce the representative models in vision-and-language navigation, including data augmentation, search strategies, training methods and action spaces. Finally, from the experiments under different data sets, we analyze the advantages and disadvantages of the existing models, and prospect some future and possible research directions.
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图 6 融合强化学习和模仿学习的过程
Fig. 6 The mixture of reinforcement learning and imitation learning
表 1 视觉语言导航不同数据集的对比
Table 1 The comparison of different datasets in vision-and-language navigation
数据集 训练集 (条) 可见验证集 (条) 不可见验证集 (条) 测试集 (条) 平均指令长度 (单词个数) 语言种类 FGR2R[7] 51 377 3 775 8 481 15 385 7.2 英语 REVERIE[4] 10 466 4 944 3 573 6 292 18.0 英语 BL-R2R[12] 14 025 1 020 2 349 4 188 20.6 英语/中文 R2R[1] 14 039 1 021 2 349 4 173 29.4 英语 R4R[8] 233 613 1 035 45 162 — 58.4 英语 R6R[9] 89 632 — 35 777 — 91.2 英语 R8R[9] 94 731 — 43 273 — 121.6 英语 RxR[10] 79 467 8 813 13 625 24 164 77.8 英语/印地语/泰卢固语 Landmark-RxR[11] 133 602 13 591 19 547 — 21.0 英语 
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表 2 视觉语言导航任务中的评价指标
Table 2 The metrics of vision-and-language navigation
评价指标 定义 公式 路径长度 起始位置到停止位置的导航轨迹长度 $\mathop {\displaystyle\sum d }\limits_{ { {\boldsymbol{v} }_i} \in V} \left( { { {\boldsymbol{v} }_i},{ {\boldsymbol{v} }_{i + 1} } } \right)$ 导航误差 预测路径终点和参考路径终点的距离 $d\left({\boldsymbol{v}}_{t},{\boldsymbol{v}}_{e}\right)$ 理想成功率 预测路径中任意节点距离参考路径终点的阈值距离内的概率 ${\mathbb{I}}\left[ {\left( {\mathop {\min }\limits_{{{\boldsymbol{v}}_i} \in V} d\left( {{{\boldsymbol{v}}_i},{{\boldsymbol{v}}_e}} \right)} \right) \le {d_{th}}} \right]$ 导航成功率 停止位置与参考路径终点的距离不大于 3 米的概率 ${\mathbb{I}}\left[ {NE({{\boldsymbol{v}}_t},{{\boldsymbol{v}}_e}) \le {d_{th}}} \right]$ 基于路径加权的成功率 基于路径长度加权的导航成功率 ${SR}({\boldsymbol{v} }_t, {\boldsymbol{v} }_e) \cdot \dfrac{d_{gt} }{\max \left\{ {PL}({ V}), d_{gt}\right\} }$ 长度加权的覆盖分数[59] 预测路径相对于参考路径的路径覆盖率和长度分数 ${PC}\left(P, R\right)\cdot LS\left( P, R\right)$ 基于动态时间规整加权成功率[59] 由成功率加权的预测路径和参考路径的时空相似性 $SR({ {\boldsymbol{v} }_t},{ {\boldsymbol{v} }_e}) \cdot \exp \left( { - \dfrac{ {\mathop {\min }\limits_{ {\boldsymbol{w} } \in W} \sum\nolimits_{\left( { {i_k},{j_k} } \right) \in {\boldsymbol{w} } } d \left( { { {\boldsymbol{r} }_{ {i_k} } },{ {\boldsymbol{q} }_{ {j_k} } } } \right)} }{ {|R| \cdot {d_{th} } } }} \right)$
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表 3 在 R2R 测试数据集上的视觉语言导航方法对比
Table 3 The comparison of vision-and-language navigation methods on the R2R test dataset
方法 路径长度 (米) SR (%) SPL (%) seq2seq[1] 8.13 20.0 18.0 RPA[48] 9.15 25 23.0 SF[13] 14.82 35 28.0 SMNA[31] 18.04 48 35 PTA[29] 10.17 40.0 36.0 RCM[30] 11.97 43.0 38.0 Regretful[23] 13.69 48.0 40.0 FAST[22] 22.08 54.0 41.0 EGP[26] — 53.0 42.0 PRESS[52] 10.77 49.0 45.0 SSM[28] 22.10 61.0 46.0 Envdrop[17] 11.66 51.0 47.0 SERL[49] — 53.0 49.0 OAAM[32] 10.40 53.0 50.0 AuxRN[44] — 55.0 51.0 PREVALENT[54] 10.51 54.0 51.0 RelGraph[27] 10.29 55.0 52.0 RecBERT[57] 12.35 63.0 57.0 HAMT[58] 12.27 65.0 60.0
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表 4 在 R4R 测试数据集上的视觉语言导航方法对比
Table 4 The comparison of vision-and-language navigation methods on the R4R test dataset
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表 5 视觉语言导航中的不同方法改进的对比
Table 5 The comparison of different improvements in vision-and-language navigation
方法 数据
增强导航
策略动作
空间训练
方法R2R
SR (%)R4R
SR (%)Seq2seq[1] — — — √ 20.0 25.7 RPA[48] — — — √ 25.0 — SF[13] √ √ √ — 35.0 23.8 SMNA[31] — √ — √ 48.0 — Regretful[23] — √ — — 48.0 30.1 FAST[22] — √ — — 54.0 — PTA[29] — — √ — 40.0 24.0 PRESS[52] — — — √ 49.0 29.0 RCM[30] — √ — √ 43.0 29.0 Envdrop[17] √ — — √ 51.0 — SERL[49] — — — √ 53.0 — OAAM[32] — — — √ 53.0 31.0 PREVALENT[54] — — — √ 54.0 — EGP[26] — √ √ — 53.0 30.2 SSM[28] — √ — √ 61.0 — RecBERT[57] — √ — √ 63.0 43.6 HAMT[58] — √ — √ 65.0 44.6
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