1 citations · 5 across the 18 of their papers we have counts for
21 papers
Navi-Agent: Unlocalized Monocular Navigation Agent
Wenyuan Xie, Mengyang Hong, Yongzhong Wang +9
Vision-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to execute long-horizon instructions in unknown environments. Existing zero-shot VLN-CE sy…
MVP-Nav: Multi-layer Value Map Planner Navigator
Wenyuan Xie, Shaokai Wu, Yijin Zhou +7
Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe p…
RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation
Guodong Zhang, Qichen He, Wenyuan Xie +6
Bridging abstract semantics and precise physical control remains a fundamental challenge in open-world robotic manipulation. While recent data-driven policies show promise, their r…
Recovering Hidden Reward in Diffusion-Based Policies
Yanbiao Ji, Qiuchang Li, Yuting Hu +7
This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar energy function whose gradient…
HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labeling
Mei Li, Huayi Zhou, Suizhi Huang +3
Regressing 3D rotations of objects from 2D images is a crucial yet challenging task, with broad applications in autonomous driving, virtual reality, and robotic control. Existing r…
MORE: Multi-Organ Medical Image REconstruction Dataset
Shaokai Wu, Yapan Guo, Yanbiao Ji +6
CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hinderi…
Dejavu: Towards Experience Feedback Learning for Embodied Intelligence
Shaokai Wu, Yanbiao Ji, Qiuchang Li +7
Embodied agents face a fundamental limitation: once deployed in real-world environments, they cannot easily acquire new knowledge to improve task performance. In this paper, we pro…
BECAME: BayEsian Continual Learning with Adaptive Model MErging
Mei Li, Yuxiang Lu, Qinyan Dai +3
Continual Learning (CL) strives to learn incrementally across tasks while mitigating catastrophic forgetting. A key challenge in CL is balancing stability (retaining prior knowledg…
DAG: Deep Adaptive and Generative -Free Community Detection on Attributed Graphs
Chang Liu, Yuwen Yang, Yue Ding +4
Community detection on attributed graphs with rich semantic and topological information offers great potential for real-world network analysis, especially user matching in online g…
Generating Negative Samples for Multi-Modal Recommendation
Yanbiao Ji, Dan Luo, Chang Liu +6
Multi-modal recommender systems (MMRS) have gained significant attention due to their ability to leverage information from various modalities to enhance recommendation quality. How…
How Does Topology Bias Distort Message Passing? A Dirichlet Energy Perspective
Yanbiao Ji, Yue Ding, Dan Luo +4
Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly under…
Discretized Gaussian Representation for Tomographic Reconstruction
Shaokai Wu, Yuxiang Lu, Yapan Guo +9
Computed Tomography (CT) enables detailed cross-sectional imaging but continues to face challenges in balancing reconstruction quality and computational efficiency. While deep lear…
From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
Yanbiao Ji, Chang Liu, Xin Chen +5
Enabling large language models (LLMs) to effectively process and reason with graph-structured data remains a significant challenge despite their remarkable success in natural langu…
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation
Yue Ding, Yanbiao Ji, Xun Cai +7
In modern recommender systems, especially in e-commerce, predicting multiple targets such as click-through rate (CTR) and post-view conversion rate (CTCVR) is common. Multi-task re…
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Weihao Jiang, Zhaozhi Xie, Yuxiang Lu +6
Mask-guided matting networks have achieved significant improvements and have shown great potential in practical applications in recent years. However, simply learning matting repre…
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
Deyi Ji, Lanyun Zhu, Siqi Gao +8
In this paper, we address the challenge of Multi-Object Tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, tur…
Task Indicating Transformer for Task-conditional Dense Predictions
Yuxiang Lu, Shalayiding Sirejiding, Bayram Bayramli +3
The task-conditional model is a distinctive stream for efficient multi-task learning. Existing works encounter a critical limitation in learning task-agnostic and task-specific rep…
YOLO-MED : Multi-Task Interaction Network for Biomedical Images
Suizhi Huang, Shalayiding Sirejiding, Yuxiang Lu +4
Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmen…
Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training
Huayi Zhou, Mukun Luo, Fei Jiang +3
The 2D human pose estimation (HPE) is a basic visual problem. However, its supervised learning requires massive keypoint labels, which is labor-intensive to collect. Thus, we aim a…
Enhancing cross-domain detection: adaptive class-aware contrastive transformer
Ziru Zeng, Yue Ding, Hongtao Lu
Recently,the detection transformer has gained substantial attention for its inherent minimal post-processing requirement.However,this paradigm relies on abundant training data,yet…
PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation
Zhaozhi Xie, Bochen Guan, Weihao Jiang +4
The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in…