2.4k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nana Liu4 profiles10 · h 14
- Wanli Ouyang3 profiles7 · h 100
- Shaoting Zhang3 profiles6 · h 48
- Wenwei Zhang2 profiles6 · h 24
- Guotai Wang2 profiles5 · h 30
- Jifeng Dai2 profiles5 · h 66
- Lewei Lu5 · h 32
- Shi Jin3 profiles5 · h 49
- Wenhai Wang2 profiles5 · h 34
- Xizhou Zhu5 · h 48
- Yu Qiao2 profiles5 · h 21
- Zhengxia Zou5 · h 42
- Beijing Academy of Artificial IntelligenceCN102 papers
- Shanghai Jiao Tong UniversityCN46 papers
- Chinese University of Hong KongHK26 papers
- Tsinghua UniversityCN25 papers
- Fudan UniversityCN21 papers
- Group Sense (China)CN17 papers
- Northwestern Polytechnical UniversityCN14 papers
- ShangHai JiAi Genetics & IVF InstituteCN14 papers
- University of Science and Technology of ChinaCN14 papers
- University of Hong KongHK13 papers
- Peking UniversityCN11 papers
- Tongji UniversityCN11 papers
18 papers · 2 filters
Visual Grounding with Multi-modal Conditional Adaptation
Ruilin Yao, Shengwu Xiong, Yichen Zhao +1
Visual grounding is the task of locating objects specified by natural language expressions. Existing methods extend generic object detection frameworks to tackle this task. They ty…
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity
Yangzhou Liu, Yue Cao, Zhangwei Gao +9
Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of Vision Large Language Models (VLLMs). However, existing visual instruction tunin…
Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding
Xianqiang Gao, Pingrui Zhang, Delin Qu +4
3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackl…
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Reuben Dorent, Roya Khajavi, Tagwa Idris +24
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentati…
Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning
Litingyu Wang, Yijie Qu, Xiangde Luo +3
Assessing the presence of potentially malignant lymph nodes aids in estimating cancer progression, and identifying surrounding benign lymph nodes can assist in determining potentia…
From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization
Ziran Zhang, Yongrui Ma, Yueting Chen +4
Video Frame Interpolation (VFI) is important for video enhancement, frame rate up-conversion, and slow-motion generation. The introduction of event cameras, which capture per-pixel…
PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction
Danpeng Chen, Hai Li, Weicai Ye +7
Recently, 3D Gaussian Splatting (3DGS) has attracted widespread attention due to its high-quality rendering, and ultra-fast training and rendering speed. However, due to the unstru…
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving
Shaoyuan Xie, Lingdong Kong, Wenwei Zhang +4
Recent advancements in bird's eye view (BEV) representations have shown remarkable promise for in-vehicle 3D perception. However, while these methods have achieved impressive resul…
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
Lingdong Kong, Xiang Xu, Jiawei Ren +5
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supe…
3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting
Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang +5
In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction with intricate detai…
Hypergraph-based Multi-View Action Recognition using Event Cameras
Yue Gao, Jiaxuan Lu, Siqi Li +2
Action recognition from video data forms a cornerstone with wide-ranging applications. Single-view action recognition faces limitations due to its reliance on a single viewpoint. I…
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
Chenyang Liu, Keyan Chen, Haotian Zhang +3
Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Re…
LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
Yushi Lan, Fangzhou Hong, Shangchen Zhou +7
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved su…
B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-Instructions
Hao Zhang, Wenqi Shao, Hong Liu +5
Large Vision-Language Models (LVLMs) have shown significant progress in responding well to visual-instructions from users. However, these instructions, encompassing images and text…
MS-Net: A Multi-Path Sparse Model for Motion Prediction in Multi-Scenes
Xiaqiang Tang, Weigao Sun, Siyuan Hu +2
The multi-modality and stochastic characteristics of human behavior make motion prediction a highly challenging task, which is critical for autonomous driving. While deep learning…
Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective
Feiyu Yao, Zongkai Wu, Li Yi
Estimating 3D full-body pose from sparse sensor data is a pivotal technique employed for the reconstruction of realistic human motions in Augmented Reality and Virtual Reality. How…
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
Junyu Gao, Liangliang Zhao, Xuelong Li
Object counting is a hot topic in computer vision, which aims to estimate the number of objects in a given image. However, most methods only count objects of a single category for…
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
Haobo Yuan, Xiangtai Li, Chong Zhou +3
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its…