Search Results for author:
Found 15 papers, 9 papers with code
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success.
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 \textbf{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 \textbf{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
We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) for enhanced scene understanding and planning capabilities.
BEV-CLIP: Multi-modal BEV Retrieval Methodology for Complex Scene in Autonomous Driving
The demand for the retrieval of complex scene data in autonomous driving is increasing, especially as passenger vehicles have been equipped with the ability to navigate urban settings, with the imperative to address long-tail scenarios.
Street Gaussians for Modeling Dynamic Urban Scenes
We introduce Street Gaussians, a new explicit scene representation that tackles all these limitations.
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.
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.
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.
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
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.
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.
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 #12 on
Image Classification
on SVHN
