最新SOTA！JanusVLN：双重隐式记忆解耦语义与空间，显著降低了计算与推理开销

Core Insights - The article introduces JanusVLN, an innovative framework for Vision-Language Navigation (VLN) that addresses the limitations of existing methods by implementing a Dual Implicit Memory paradigm, which decouples visual semantics and spatial geometry [2][19]. Background on Current VLN Memory Mechanisms - Current VLN methods face three main challenges: spatial information distortion and loss due to reliance on text cognitive maps, low computational and reasoning efficiency from storing historical image frames, and memory inflation leading to "memory explosion" issues [3][5]. Key Innovations of JanusVLN - JanusVLN introduces a Dual Implicit Memory framework inspired by human cognitive science, effectively separating semantic memory from spatial geometric memory [7][19]. - The framework utilizes a pre-trained 3D visual geometry model (VGGT) to derive spatial geometric information from single RGB video streams, enhancing the model's spatial perception capabilities [8][19]. - A mixed incremental update strategy is employed to maintain a fixed-size memory, significantly improving reasoning efficiency by avoiding redundant computations [8][11]. Methodology Overview - JanusVLN consists of three main components: a dual encoder architecture for visual perception, a dual implicit neural memory system, and a hybrid incremental update strategy [10][11]. - The dual encoder architecture includes a 2D visual semantic encoder and a 3D spatial geometric encoder, working together to provide comprehensive scene understanding [11]. Experimental Results - JanusVLN has been evaluated on two authoritative VLN benchmarks, VLN-CE and RxR-CE, achieving state-of-the-art (SOTA) performance [15]. - The framework demonstrates superior performance in spatial reasoning tasks, successfully completing complex navigation challenges [18][21]. Quantitative Analysis - JanusVLN shows significant improvements in success rate (SR) metrics, outperforming advanced methods that rely on expensive inputs by 10.5 to 35.5 percentage points [21]. - Compared to other SOTA methods using RGB input with explicit memory, JanusVLN achieves a 3.6 to 10.8 percentage point improvement in SR, validating the effectiveness of the Dual Implicit Memory paradigm [21].