80 citations · 471 across the 61 of their papers we have counts for
61 papers
Efficient View Synthesis with Neural Radiance Distribution Field
Yushuang Wu, Xiao Li, Jinglu Wang +3
Recent work on Neural Radiance Fields (NeRF) has demonstrated significant advances in high-quality view synthesis. A major limitation of NeRF is its low rendering efficiency due to…
LATR: 3D Lane Detection from Monocular Images with Transformer
Yueru Luo, Chaoda Zheng, Xu Yan +4
3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye vi…
YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection
Yuncheng Jiang, Zixun Zhang, Ruimao Zhang +3
Accurate polyp detection is essential for assisting clinical rectal cancer diagnoses. Colonoscopy videos contain richer information than still images, making them a valuable resour…
WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
Jun Wei, Yiwen Hu, Shuguang Cui +2
Limited by expensive pixel-level labels, polyp segmentation models are plagued by data shortage and suffer from impaired generalization. In contrast, polyp bounding box annotations…
Universal Semi-supervised Model Adaptation via Collaborative Consistency Training
Zizheng Yan, Yushuang Wu, Yipeng Qin +3
In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained sou…
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
Yu Lu, Junwei Bao, Zichen Ma +4
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existin…
Heterogeneous Value Alignment Evaluation for Large Language Models
Zhaowei Zhang, Ceyao Zhang, Nian Liu +5
The emergent capabilities of Large Language Models (LLMs) have made it crucial to align their values with those of humans. However, current methodologies typically attempt to assig…
SCoDA: Domain Adaptive Shape Completion for Real Scans
Yushuang Wu, Zizheng Yan, Ce Chen +6
3D shape completion from point clouds is a challenging task, especially from scans of real-world objects. Considering the paucity of 3D shape ground truths for real scans, existing…
Hierarchical Weight Averaging for Deep Neural Networks
Xiaozhe Gu, Zixun Zhang, Yuncheng Jiang +4
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs). Among various attempts to improve SGD, weight aver…
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling
Yujian Zheng, Zirong Jin, Moran Li +4
In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using…
MVImgNet: A Large-scale Dataset of Multi-view Images
Xianggang Yu, Mutian Xu, Yidan Zhang +10
Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer…
Efficient Large-scale Scene Representation with a Hybrid of High-resolution Grid and Plane Features
Yuqi Zhang, Guanying Chen, Shuguang Cui
Existing neural radiance fields (NeRF) methods for large-scale scene modeling require days of training using multiple GPUs, hindering their applications in scenarios with limited c…
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Chaoda Zheng, Xu Yan, Haiming Zhang +4
3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance match…
Adaptive Context Selection for Polyp Segmentation
Ruifei Zhang, Guanbin Li, Zhen Li +3
Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and…
Benchmarking the Robustness of LiDAR Semantic Segmentation Models
Xu Yan, Chaoda Zheng, Ying Xue +3
When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect t…
BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations
Jun Wei, Yiwen Hu, Guanbin Li +3
Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing pol…
Geometry-Aware Network for Domain Adaptive Semantic Segmentation
Yinghong Liao, Wending Zhou, Xu Yan +3
Measuring and alleviating the discrepancies between the synthetic (source) and real scene (target) data is the core issue for domain adaptive semantic segmentation. Though recent w…
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
Shuliang Ning, Mengcheng Lan, Yanran Li +5
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to…
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking
Haoning Zhang, Junwei Bao, Haipeng Sun +4
Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all histor…
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking
Haoning Zhang, Junwei Bao, Haipeng Sun +3
Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from…
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Xu Yan, Heshen Zhan, Chaoda Zheng +4
Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we t…
APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation
Yuncheng Jiang, Zixun Zhang, Shixi Qin +3
In 3D medical image segmentation, small targets segmentation is crucial for diagnosis but still faces challenges. In this paper, we propose the Axis Projection Attention UNet, name…
M-3DLaneNet: Exploring Multi-Modal 3D Lane Detection
Yueru Luo, Xu Yan, Chaoda Zheng +5
Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. Previous works mainly focused on using images for 3D lane detection, leading to…
Where Are You Looking?: A Large-Scale Dataset of Head and Gaze Behavior for 360-Degree Videos and a Pilot Study
Yili Jin, Junhua Liu, Fangxin Wang +1
360° videos in recent years have experienced booming development. Compared to traditional videos, 360° videos are featured with uncertain user behaviors, bringing opportunities as…