REVERIE
Emerging20papers using it
2020first seen
Dataset Card for REVERIE Dataset Details Dataset Type: REVERIE is the first large-scale visual instruction-tuning dataset with ReflEctiVE RatIonalE annotations. REVERIE comprises 115k machine-generated reasoning instructions, each meticulously annotated with a corresponding pair of correct and confusing responses, alon
Papers using REVERIE (20)
- ProFocus: Proactive Perception and Focused Reasoning in Vision-and-Language NavigationImplicit Geometry Representations for Vision-and-Language Navigation from Web VideosTrajectory-Diversity-Driven Robust Vision-and-Language NavigationBeyond Textual Knowledge-Leveraging Multimodal Knowledge Bases for Enhancing Vision-and-Language NavigationEnhancing Vision-Language Navigation with Multimodal Event Knowledge from Real-World Indoor Tour VideosLearning Goal-Oriented Vision-and-Language Navigation with Self-Improving Demonstrations at ScaleThink Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion
and Reasoning for Vision-and-Language NavigationA Recurrent Vision-and-Language BERT for NavigationVision-and-Language Navigation via Causal LearningHistory Aware Multimodal Transformer for Vision-and-Language NavigationThe Road to Know-Where: An Object-and-Room Informed Sequential BERT for
Indoor Vision-Language NavigationHOP: History-and-Order Aware Pre-training for Vision-and-Language
NavigationCLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language NavigationVisual-Language Navigation Pretraining via Prompt-based Environmental
Self-explorationVLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language
NavigationPrompt-based Context- and Domain-aware Pretraining for Vision and
Language NavigationDAP: Domain-aware Prompt Learning for Vision-and-Language NavigationCausality-based Cross-Modal Representation Learning for
Vision-and-Language NavigationWhy Only Text: Empowering Vision-and-Language Navigation with
Multi-modal PromptsSeeing is Believing? Enhancing Vision-Language Navigation using Visual
Perturbations