To address the occlusion issues in person Re-Identification (ReID) tasks, many methods have been proposed to extract part features by introducing external spatial information. However, due to missing part appearance information caused by occlusion and noisy spatial information from external model, these purely vision-based approaches fail to correctly learn the features of human body parts from limited training data and struggle in accurately locating body parts, ultimately leading to misaligned part features. To tackle these challenges, we propose a Prompt-guided Feature Disentangling method (ProFD), which leverages the rich pre-trained knowledge in the textual modality facilitate model to generate well-aligned part features. ProFD first designs part-specific prompts and utilizes noisy segmentation mask to preliminarily align visual and textual embedding, enabling the textual prompts to have spatial awareness. Furthermore, to alleviate the noise from external masks, ProFD adopts a hybrid-attention decoder, ensuring spatial and semantic consistency during the decoding process to minimize noise impact. Additionally, to avoid catastrophic forgetting, we employ a self-distillation strategy, retaining pre-trained knowledge of CLIP to mitigate over-fitting. Evaluation results on the Market1501, DukeMTMC-ReID, Occluded-Duke, Occluded-ReID, and P-DukeMTMC datasets demonstrate that ProFD achieves state-of-the-art results.

Vision-and-Language Navigation (VLN) requires embodied agents to ground natural language instructions in visual perception and make navigation decisions in complex 3D environments, making it a central problem in embodied artificial intelligence. Since the introduction of the Room-to-Room (R2R) benchmark, VLN has made substantial progress. In recent years, as research settings have gradually expanded from closed and single indoor benchmark scenarios to open-world environments, the field has undergone a profound paradigm shift from passive instruction following on fixed benchmarks to autonomous cognitive navigation in open-world settings. However, existing surveys mainly organize prior work according to technical taxonomies, lacking a systematic characterization of this paradigm evolution. To address this gap, this survey proposes an evolution-centered unified analytical framework that reviews contemporary VLN research across four progressive layers: perception, cognition, learning, and generalization. It reveals the intrinsic connections and evolutionary logic among different technical lines, identifies key open challenges at each dimension, and outlines future research directions. This survey aims to provide VLN researchers with a clear panoramic view of capability evolution, while offering the broader embodied intelligence community a systematic roadmap from closed-benchmark evaluation toward trustworthy open-world deployment.

Recent advances in embodied artificial intelligence have enabled robot manipulation agents to transition from executing static policies to autonomously constructing reusable skills, programs, and behavioral artifacts. However, successful execution, retained utility, and repeatable acquisition support fundamentally different empirical claims. This mechanism-oriented analysis critically examines 162 purposively selected primary studies through two complementary questions: how agent decisions reach robot control, and how capabilities are acquired and retained. We connect reported gains to their underlying artifacts, recipients, comparators, and evaluation boundaries. Retained programs and guidance can improve skill selection, command formulation, and recovery without altering the underlying actor, whereas neural adaptation and retention mechanisms govern distinct system components. However, aggregate performance gains can mask condition-specific losses or complete task failures. Applying our proposed five-level capability framework reveals that while intermediate levels of modular and simulation-based retention are widespread, no inspected system satisfies the Level-5 criterion of autonomous physical self-improvement under rigorous human- and procedural-intervention accounting. Although repeatable acquisition, transfer, and retention co-occur in bounded simulation under stated interaction budgets, independently verified hardware self-improvement with connected transfer, retention, and full physical cost accounting remains unresolved. These findings motivate standardized evaluation protocols that explicitly couple retained capability gains with repeatable acquisition and rigorous intervention-cost accounting.