29 citations · 86 across the 30 of their papers we have counts for
20 papers · 1 filter
MemPose: Category-level Object Pose Estimation with Memory
Xiao Lin, Minghao Zhu, Yun Peng +4
In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data…
P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation
Kai Sheng, Liuyi Wang, Haojie Dai +5
Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen environments. Existing zero-sho…
Temporal-Guided Visual Foundation Models for Event-Based Vision
Ruihao Xia, Junhong Cai, Luziwei Leng +5
Event cameras offer unique advantages for vision tasks in challenging environments, yet processing asynchronous event streams remains an open challenge. While existing methods rely…
CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation
Xiao Lin, Yun Peng, Liuyi Wang +6
Category-level object pose estimation aims to recover the rotation, translation and size of unseen instances within predefined categories. In this task, deep neural network-based m…
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer
Minghao Zhu, Zhengpu Wang, Mengxian Hu +5
Transferring visual-language knowledge from large-scale foundation models for video recognition has proved to be effective. To bridge the domain gap, additional parametric modules…
RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
Jianxin Huang, Jiahang Li, Ning Jia +4
Task-specific data-fusion networks have marked considerable achievements in urban scene parsing. Among these networks, our recently proposed RoadFormer successfully extracts hetero…
MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Mengjiao Shen +3
Despite the remarkable developments of recent large models in Embodied Artificial Intelligence (E-AI), their integration into robotics is hampered by their excessive parameter size…
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
Yun Peng, Xiao Lin, Nachuan Ma +4
Visual anomaly detection is vital in real-world applications, such as industrial defect detection and medical diagnosis. However, most existing methods focus on local structural an…
Efficient Text-driven Motion Generation via Latent Consistency Training
Mengxian Hu, Minghao Zhu, Xun Zhou +4
Text-driven human motion generation based on diffusion strategies establishes a reliable foundation for multimodal applications in human-computer interactions. However, existing ad…
Vision-and-Language Navigation via Causal Learning
Liuyi Wang, Zongtao He, Ronghao Dang +3
In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navig…
Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Ronghao Dang +3
Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios. However, existing VLN metho…
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge
Xiao Lin, Minghao Zhu, Ronghao Dang +5
Most of existing category-level object pose estimation methods devote to learning the object category information from point cloud modality. However, the scale of 3D datasets is li…
TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer
Xiao Lin, Deming Wang, Guangliang Zhou +2
Estimating the 6D object pose is an essential task in many applications. Due to the lack of depth information, existing RGB-based methods are sensitive to occlusion and illuminatio…
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning
Minghao Zhu, Xiao Lin, Ronghao Dang +2
As the most essential property in a video, motion information is critical to a robust and generalized video representation. To inject motion dynamics, recent works have adopted fra…
A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Jiagui Tang +4
Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancem…
PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation
Liuyi Wang, Chengju Liu, Zongtao He +4
Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of…
MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation
Zongtao He, Liuyi Wang, Shu Li +3
Vision-and-Language Navigation (VLN) aims to develop intelligent agents to navigate in unseen environments only through language and vision supervision. In the recently proposed co…
Unbiased Directed Object Attention Graph for Object Navigation
Ronghao Dang, Zhuofan Shi, Liuyi Wang +3
Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explor…
PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds
Sukai Wang, Yuxiang Sun, Chengju Liu +1
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filte…
Focal Loss in 3D Object Detection
Peng Yun, Lei Tai, Yuan Wang +2
3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larg…