24 citations · 57 across the 14 of their papers we have counts for
14 papers
OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation
Kuanning Wang, Ke Fan, Chenhao Qiu +5
Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes. However, existing VLA models…
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfer
Kuanning Wang, Ke Fan, Yuqian Fu +6
We present OCRA, an Object-Centric framework for video-based human-to-Robot Action transfer that learns directly from human demonstration videos to enable robust manipulation. Obje…
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
Linhai Zhuo, Zheng Wang, Yuqian Fu +1
The source-free cross-domain few-shot learning (CD-FSL) task aims to transfer pretrained models to target domains utilizing minimal samples, eliminating the need for source domain…
ObjectRelator: Enabling Cross-View Object Relation Understanding Across Ego-Centric and Exo-Centric Perspectives
Yuqian Fu, Runze Wang, Bin Ren +6
Bridging the gap between ego-centric and exo-centric views has been a long-standing question in computer vision. In this paper, we focus on the emerging Ego-Exo object corresponden…
MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Dataset
Jianxiong Gao, Yanwei Fu, Yuqian Fu +3
Reconstructing 3D visuals from functional Magnetic Resonance Imaging (fMRI) data, introduced as Recon3DMind, is of significant interest to both cognitive neuroscience and computer…
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
Yuqian Fu, Yu Wang, Yixuan Pan +7
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.…
Open-Vocabulary Video Relation Extraction
Wentao Tian, Zheng Wang, Yuqian Fu +2
A comprehensive understanding of videos is inseparable from describing the action with its contextual action-object interactions. However, many current video understanding tasks pr…
MinD-3D: Reconstruct High-quality 3D objects in Human Brain
Jianxiong Gao, Yuqian Fu, Yun Wang +3
In this paper, we introduce Recon3DMind, an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals, marking a significant adva…
On the Importance of Spatial Relations for Few-shot Action Recognition
Yilun Zhang, Yuqian Fu, Xingjun Ma +4
Deep learning has achieved great success in video recognition, yet still struggles to recognize novel actions when faced with only a few examples. To tackle this challenge, few-sho…
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Yuqian Fu, Yu Xie, Yanwei Fu +1
Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the…
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
Linhai Zhuo, Yuqian Fu, Jingjing Chen +2
Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target d…
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
Yuqian Fu, Yu Xie, Yanwei Fu +2
Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core cha…
Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos
Yuqian Fu, Yanwei Fu, Yu-Gang Jiang
Given a video demonstration, can we imitate the action contained in this video? In this paper, we introduce a novel task, dubbed mesh-based action imitation. The goal of this task…