8 citations · 27 across the 24 of their papers we have counts for
27 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…
GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture
GigaBrain Team, Angen Ye, Axiang Sun +56
Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured sett…
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…
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…
Few-shot Implicit Function Generation via Equivariance
Suizhi Huang, Xingyi Yang, Hongtao Lu +1
Implicit Neural Representations (INRs) have emerged as a powerful framework for representing continuous signals. However, generating diverse INR weights remains challenging due to…
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…
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…
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
Yuwen Yang, Yuxiang Lu, Suizhi Huang +3
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model traini…
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…
FedHCA: Towards Hetero-Client Federated Multi-Task Learning
Yuxiang Lu, Suizhi Huang, Yuwen Yang +3
Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tas…
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
Yuwen Yang, Chang Liu, Xun Cai +3
Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, w…
Position-Aware Subgraph Neural Networks with Data-Efficient Learning
Chang Liu, Yuwen Yang, Zhe Xie +2
Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs wi…