Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

MeSS: 基于城市网格的户外场景生成与跨视角一致扩散

MeSS: City Mesh-Guided Outdoor Scene Generation with Cross-View Consistent Diffusion

MeSS通过改进跨视角一致性，利用城市网格模型生成高质量且风格一致的户外场景，并结合3D高斯点划重建技术提升生成质量。

Purifying Real Images with an Attention-guided Style Transfer Network for Gaze Estimation

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the reali…

面向航拍图像目标导航的不确定性感知世界模型

Uncertainty-Aware World Model for Aerial Image-Goal Navigation

针对航拍图像目标导航中现有世界模型的未来状态预测不足问题，提出UA-NWM模型，将轨迹评分建模为条件分布外检测，仅用不可解释残差评分，实验验证其性能与实用性。

MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents

We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions …

HyperStyler：基于上下文感知风格导航与超网络的低资源作者风格迁移

HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks

HyperStyler通过解耦风格选择与实现的架构，在低资源作者风格迁移任务中，以仅增2.4%参数的规模实现优于现有方法的性能，且推理速度比LLM快1.8倍以上。

Diagnosing Vision-and-Language Navigation: What Really Matters

Vision-and-language navigation (VLN) is a multimodal task where an agent follows natural language instructions and navigates in visual environments. Multiple setups have been proposed, and researchers apply new model architectures or training techniques to boost navigation performance. However, there still exist non-negligible gaps between machines' performance and human benchmarks. Moreover, the agents' inner mechanisms for navigation decisions remain unclear. To the best of our knowledge, how the agents perceive the multimodal input is under-studied and needs investigation. In this work, we …

为盲人和低视力人群生成上下文相关的导航指令

Generating Contextually-Relevant Navigation Instructions for Blind and Low Vision People

本研究针对盲人和低视力人群的导航难题，构建多场景图像目标数据集，探究具身指令生成方法，验证了大模型生成的导航指令的有效性，并总结了BLV用户的场景化指令偏好。

LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action

Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings. However, particularly in vision-based settings where specifying goals requires an image, this makes for an unnatural interface. Language provides a more convenient modality for communication with robots, but contemporary methods typically require expensive supervision, in the form of trajectories annotated with language descriptions. We present a system, LM-Nav, for robotic navigation that enjoys the benefits of training on unannotated large …