Yunbo Xu

Xuesong Zhang, Jia Li, Yunbo Xu et al.

Autonomous navigation guided by natural language instructions in embodied environments remains a challenge for vision-language navigation (VLN) agents. Although recent advancements in learning diverse and fine-grained visual environmental representations have shown promise, the fragile performance improvements may not conclusively attribute to enhanced visual grounding,a limitation also observed in related vision-language tasks. In this work, we preliminarily investigate whether advanced VLN models genuinely comprehend the visual content of their environments by introducing varying levels of visual perturbations. These perturbations include ground-truth depth images, perturbed views and random noise. Surprisingly, we experimentally find that simple branch expansion, even with noisy visual inputs, paradoxically improves the navigational efficacy. Inspired by these insights, we further present a versatile Multi-Branch Architecture (MBA) designed to delve into the impact of both the branch quantity and visual quality. The proposed MBA extends a base agent into a multi-branch variant, where each branch processes a different visual input. This approach is embarrassingly simple yet agnostic to topology-based VLN agents. Extensive experiments on three VLN benchmarks (R2R, REVERIE, SOON) demonstrate that our method with optimal visual permutations matches or even surpasses state-of-the-art results. The source code is available at here.

Yunbo Xu, Xuesong Zhang, Jia Li et al.

Following language instructions, vision-language navigation (VLN) agents are tasked with navigating unseen environments. While augmenting multifaceted visual representations has propelled advancements in VLN, the significance of foreground and background in visual observations remains underexplored. Intuitively, foreground regions provide semantic cues, whereas the background encompasses spatial connectivity information. Inspired on this insight, we propose a Consensus-driven Online Feature Augmentation strategy (COFA) with alternative foreground and background features to facilitate the navigable generalization. Specifically, we first leverage semantically-enhanced landmark identification to disentangle foreground and background as candidate augmented features. Subsequently, a consensus-driven online augmentation strategy encourages the agent to consolidate two-stage voting results on feature preferences according to diverse instructions and navigational locations. Experiments on REVERIE and R2R demonstrate that our online foreground-background augmentation boosts the generalization of baseline and attains state-of-the-art performance.

Xuesong Zhang, Yunbo Xu, Jia Li et al.

Navigating unseen environments from natural language instructions remains challenging for egocentric agents in Vision-and-Language Navigation (VLN). Humans naturally ground concrete semantic knowledge within spatial layouts during indoor navigation. Although prior work has introduced diverse environment representations to improve reasoning, auxiliary modalities are often naively concatenated with RGB features, which underutilizes each modality's distinct contribution. We propose a hierarchical Semantic Understanding and Spatial Awareness (SUSA) architecture to enable agents to perceive and ground environments at multiple scales. Specifically, the Textual Semantic Understanding (TSU) module supports local action prediction by generating view-level descriptions, capturing fine-grained semantics and narrowing the modality gap between instructions and environments. Complementarily, the Depth Enhanced Spatial Perception (DSP) module incrementally builds a trajectory-level depth exploration map, providing a coarse-grained representation of global spatial layout. Extensive experiments show that the hierarchical representation enrichment of SUSA significantly improves navigation performance over the baseline on discrete VLN benchmarks (REVERIE, R2R, and SOON) and generalizes better to the continuous R2R-CE benchmark.