Search Results for author:
Found 8 papers, 3 papers with code
Vision-and-Language Navigation via Causal Learning
In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents, hindering their performance in unseen environments.
Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios.
PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation
The experimental results demonstrate that PASTS outperforms all existing speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset.
A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues.
MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation
For a better performance in continuous VLN, we design a multi-level instruction understanding procedure and propose a novel model, Multi-Level Attention Network (MLANet).
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation
We propose a meta-ability decoupling (MAD) paradigm, which brings together various object navigation methods in an architecture system, allowing them to mutually enhance each other and evolve together.
Search for or Navigate to? Dual Adaptive Thinking for Object Navigation
After seeing the target, we remember the target location and navigate to.
Unbiased Directed Object Attention Graph for Object Navigation
Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias.
