Challenge: Existing work on vision and language navigation relies on navigation-related losses to establish the connection between vision and modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.
Approach: They propose to provide indirect supervision to the navigation agent through a hint generator that generates visual descriptions during navigation.
Outcome: The proposed method improves the navigation performance and interpretability of the R2R and R4R datasets.

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Challenge: Existing Transformer-based VLN agents entangle orientation and vision information, which limits the learning of each information source.
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Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)

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Challenge: Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models.
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VLN-Trans: Translator for the Vision and Language Navigation Agent (2023.acl-long)

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Challenge: We observe two kinds of instructions that make the grounding in the vision-and-language navigation task quite challenging.
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On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning (2024.emnlp-main)

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Challenge: Recent advances in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility.
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Sub-Instruction Aware Vision-and-Language Navigation (2020.emnlp-main)

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Challenge: Despite significant advances, few previous works are able to fully utilize the strong correspondence between visual and textual sequences.
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On the Evaluation of Vision-and-Language Navigation Instructions (2021.eacl-main)

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Challenge: Existing instruction generators have not been evaluated using human wayfinders . BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions.
Approach: They propose an instruction-trajectory compatibility model that operates without reference instructions to improve wayfinding performance.
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Diagnosing Vision-and-Language Navigation: What Really Matters (2022.naacl-main)

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Challenge: Existing models claim to be able to align object tokens with specific visual targets, but there are non-negligible gaps between the two.
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Semantic Map-based Generation of Navigation Instructions (2024.lrec-main)

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Challenge: Existing approaches to navigation instruction generation use a sequence of panorama images as visual input.
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GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning (2026.findings-acl)

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Challenge: Current methods for instruction generation depend on privileged inputs such as semantic maps, landmark annotations, and panoramic views.
Approach: They propose a task that generates coherent navigation instructions from egocentric visual observations.
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How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)

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Challenge: Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models.
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