Search Results for author:
Found 13 papers, 3 papers with code
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
Efficient View Synthesis with Neural Radiance Distribution Field
Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF.
Universal Semi-supervised Model Adaptation via Collaborative Consistency Training
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 source model, ii) allows the source and target domain to have different label sets, i. e., they share a common label set and hold their own private label set, and iii) requires only a few labeled samples in each class of the target domain.
SCoDA: Domain Adaptive Shape Completion for Real Scans
Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data.
MVImgNet: A Large-scale Dataset of Multi-view Images
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images
On synthetic data, our Intersection-Over-Union (IoU) achieves to 93. 5%, 18% higher compared with PIFuHD.
Multi-level Consistency Learning for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.
Ranked #1 on
Semi-supervised Domain Adaptation
on VisDA2017
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds
Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated.
Representation Learning
Weakly supervised Semantic Segmentation
+1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
Ranked #20 on
Anomaly Detection
on MVTec AD
Unsupervised Anomaly Detection
Weakly Supervised Defect Detection
Pixel-level Intra-domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
LapsCore: Language-Guided Person Search via Color Reasoning
The key point of language-guided person search is to construct the cross-modal association between visual and textual input.
