29 citations · 51 across the 12 of their papers we have counts for
12 papers
EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval
Jiashi Lin, Changhong Jiang, Xiangru Lin +12
Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introd…
OptiMVMap: Offline Vectorized Map Construction via Optimal Multi-vehicle Perspectives
Zedong Dan, Zijie Wang, Wei Zhang +6
Offline vectorized maps constitute critical infrastructure for high-precision autonomous driving and mapping services. Existing approaches rely predominantly on single ego-vehicle…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
Towards 3D Object-Centric Feature Learning for Semantic Scene Completion
Weihua Wang, Yubo Cui, Xiangru Lin +2
Vision-based 3D Semantic Scene Completion (SSC) has received growing attention due to its potential in autonomous driving. While most existing approaches follow an ego-centric para…
Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
Jiaming Li, Xiangru Lin, Wei Zhang +6
Current semi-supervised object detection (SSOD) algorithms typically assume class balanced datasets (PASCAL VOC etc.) or slightly class imbalanced datasets (MS-COCO, etc). This ass…
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Jiacheng Zhang, Jiaming Li, Xiangru Lin +6
We delve into pseudo-labeling for semi-supervised monocular 3D object detection (SSM3OD) and discover two primary issues: a misalignment between the prediction quality of 3D and 2D…
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Jiacheng Zhang, Xiangru Lin, Wei Zhang +6
We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo…
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
Chang Liu, Weiming Zhang, Xiangru Lin +6
With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that…
I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation
Haoyu Ma, Xiangru Lin, Yizhou Yu
Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image stati…
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Haoyu Ma, Xiangru Lin, Zifeng Wu +1
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/disc…
Scene-Intuitive Agent for Remote Embodied Visual Grounding
Xiangru Lin, Guanbin Li, Yizhou Yu
Humans learn from life events to form intuitions towards the understanding of visual environments and languages. Envision that you are instructed by a high-level instruction, "Go t…
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
Weifeng Ge, Xiangru Lin, Yizhou Yu
Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image cla…