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.
Seeing vs. Believing: Evaluating the Language Bias of Open-Source MLLMs in Counter-Intuitive Scenes
Zhejiang University（浙江大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；PCALab, VCIP, College of Computer Science, Nankai University（南开大学计算机学院PCALab, VCIP）
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Boston University（波士顿大学）
Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所信息安全国家重点实验室）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
Seeing is Believing: Aligning Prompt Rewriting with Visual Anchors for Text-to-Image Generation
Peking University（北京大学）；Tencent（腾讯）；Dalian University of Technology（大连理工大学）；Nanyang Technological University（南洋理工大学）；University of Cambridge（剑桥大学）；Zhejiang University（浙江大学）
Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；The University of Sydney（悉尼大学）
Seeing Through Uncertainty: Free-Energy-Inspired Real-Time Adaptation for Robust Visual Navigation
Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（信息与通信研究院（I2R）、科技研究局（A*STAR））；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
