Search Results for author:
Found 28 papers, 13 papers with code
Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained Tiling
3D Gaussian Splatting (3DGS) is increasingly attracting attention in both academia and industry owing to its superior visual quality and rendering speed.
StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models
This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data.
ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration
This is complemented by a progressive data update strategy designed to ensure high-quality rendering for more complex maneuvers.
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
By providing a dynamic and realistic simulation environment, DrivingSphere enables comprehensive testing and validation of autonomous driving algorithms, ultimately advancing the development of more reliable autonomous cars.
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
These planners promise better generalizations on complicated and few-shot cases than previous methods.
DiVE: DiT-based Video Generation with Enhanced Control
Generating high-fidelity, temporally consistent videos in autonomous driving scenarios faces a significant challenge, e. g. problematic maneuvers in corner cases.
Lesion-aware network for diabetic retinopathy diagnosis
To this end, we propose a CNN-based DR diagnosis network with attention mechanism involved, termed lesion-aware network, to better capture lesion information from imbalanced data.
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion
Despite growing interest in T2M, few methods focus on safeguarding these models against adversarial attacks, with existing work on text-to-image models proving insufficient for the unique motion domain.
High-Resolution Cloud Detection Network
The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks.
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Secondly, SACN uniquely integrates the graph's structural information to achieve strong-to-strong consensus learning, improving the utilization of unlabeled data while maintaining multiview learning.
3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views
(1) \textbf{High-Volume}: 2, 500 cars are meticulously scanned by 3D scanners, obtaining car images and point clouds with real-world dimensions; (2) \textbf{High-Quality}: Each car is captured in an average of 200 dense, high-resolution 360-degree RGB-D views, enabling high-fidelity 3D reconstruction; (3) \textbf{High-Diversity}: The dataset contains various cars from over 100 brands, collected under three distinct lighting conditions, including reflective, standard, and dark.
UA-Track: Uncertainty-Aware End-to-End 3D Multi-Object Tracking
Secondly, we propose an Uncertainty-guided Query Denoising strategy to further enhance the training process.
Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation
Instead of randomly generating new data, we further design a sampling policy to let Delphi generate new data that are similar to those failure cases to improve the sample efficiency.
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
To address this, we employ information entropy neural estimation to utilize the potential of unlabeled samples.
TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes
However, the exploration of 3D dense captioning in outdoor scenes is hindered by two major challenges: 1) the domain gap between indoor and outdoor scenes, such as dynamics and sparse visual inputs, makes it difficult to directly adapt existing indoor methods; 2) the lack of data with comprehensive box-caption pair annotations specifically tailored for outdoor scenes.
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
A primary hurdle of autonomous driving in urban environments is understanding complex and long-tail scenarios, such as challenging road conditions and delicate human behaviors.
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving
Furthermore, there lack of well-formed retrieval datasets for effective evaluation.
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban street scenes.
Self-Contrastive Graph Diffusion Network
AttM aggregates higher-order structure and feature information to get an excellent embedding, while DiFM balances the state of each node in the graph through Laplacian diffusion learning and allows the cooperative evolution of adjacency and feature information in the graph.
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples.
Entropy Neural Estimation for Graph Contrastive Learning
In this paper, we theoretically illustrate that the entropy of a dataset can be approximated by maximizing the lower bound of the mutual information across different views of a graph, \ie, entropy is estimated by a neural network.
Curriculum Knowledge Switching for Pancreas Segmentation
Pancreas segmentation is challenging due to the small proportion and highly changeable anatomical structure.
Stationary Diffusion State Neural Estimation for Multiview Clustering
Meanwhile, instead of using auto-encoder in most unsupervised learning graph neural networks, SDSNE uses a co-supervised strategy with structure information to supervise the model learning.
Ranked #1 on
Multiview Clustering
on Multilingual Reuters
Graph View-Consistent Learning Network
The supervised loss uses the known labeled set, while a view-consistent loss is applied to the two views to obtain the consistent representation and a pseudo-label loss is designed by using the common high-confidence predictions.
Dual Graph Complementary Network
We use two different branches, and inputs of the two branches are the same, which are composed of structure and feature information.
Mutual Teaching for Graph Convolutional Networks
We propose a new training method named as mutual teaching, i. e., we train dual models and let them teach each other during each batch.
Ranked #1 on
Node Classification
on CiteSeer (0.5%)
Generative approach to unsupervised deep local learning
Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss.
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
For most state-of-the-art architectures, Rectified Linear Unit (ReLU) becomes a standard component accompanied with each layer.
Ranked #11 on
Image Classification
on SVHN
