20 citations · 86 across the 20 of their papers we have counts for
14 papers · 1 filter
RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training
Yunshuang Nie, Bingqian Lin, Minzhe Niu +7
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to so…
EvolveNav: Empowering LLM-Based Vision-Language Navigation via Self-Improving Embodied Reasoning
Bingqian Lin, Yunshuang Nie, Khun Loun Zai +10
Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for enhancing vision-language navigation (VLN) pe…
Unseen from Seen: Rewriting Observation-Instruction Using Foundation Models for Augmenting Vision-Language Navigation
Ziming Wei, Bingqian Lin, Yunshuang Nie +4
Data scarcity is a long-standing challenge in the Vision-Language Navigation (VLN) field, which extremely hinders the generalization of agents to unseen environments. Previous work…
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning
Xiwen Liang, Min Lin, Weiqi Ruan +6
Existing methods for vision-language task planning excel in short-horizon tasks but often fall short in complex, long-horizon planning within dynamic environments. These challenges…
Correctable Landmark Discovery via Large Models for Vision-Language Navigation
Bingqian Lin, Yunshuang Nie, Ziming Wei +5
Vision-Language Navigation (VLN) requires the agent to follow language instructions to reach a target position. A key factor for successful navigation is to align the landmarks imp…
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning
Bingqian Lin, Yanxin Long, Yi Zhu +4
Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN…
NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning
Bingqian Lin, Yunshuang Nie, Ziming Wei +6
Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural languag…
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
Mingjie Li, Bingqian Lin, Zicong Chen +3
Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced da…
Actional Atomic-Concept Learning for Demystifying Vision-Language Navigation
Bingqian Lin, Yi Zhu, Xiaodan Liang +2
Vision-Language Navigation (VLN) is a challenging task which requires an agent to align complex visual observations to language instructions to reach the goal position. Most existi…
ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts
Bingqian Lin, Yi Zhu, Zicong Chen +3
Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially…
Contrastive Instruction-Trajectory Learning for Vision-Language Navigation
Xiwen Liang, Fengda Zhu, Yi Zhu +3
The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works learn to navigate step-by-step follo…
Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation
Bingqian Lin, Yi Zhu, Yanxin Long +3
Language instruction plays an essential role in the natural language grounded navigation tasks. However, navigators trained with limited human-annotated instructions may have diffi…
Vision-Dialog Navigation by Exploring Cross-modal Memory
Yi Zhu, Fengda Zhu, Zhaohuan Zhan +4
Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help w…
Jointly Deep Multi-View Learning for Clustering Analysis
Bingqian Lin, Yuan Xie, Yanyun Qu +2
In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignmen…