Search Results for author:
Found 14 papers, 6 papers with code
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention
3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description.
StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields
In this paper, we introduce StyleDyRF, a method that represents the 4D feature space by deforming a canonical feature volume and learns a linear style transformation matrix on the feature volume in a data-driven fashion.
Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation
Moreover, to facilitate disentangled representation learning, we introduce four well-designed constraints: an auxiliary style classifier, an auxiliary inverse classifier, a content contrastive loss, and a pair of latent cycle losses, which can effectively contribute to the construction of the identity-related style space and semantic-related content space.
A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis
Fault diagnosis of rotating machinery plays a important role for the safety and stability of modern industrial systems.
CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo
The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions.
PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations.
PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-Modal Distillation and Super-Voxel Clustering
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations.
Semi-supervised Deep Multi-view Stereo
For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap.
Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark
Face sketch generation has attracted much attention in the field of visual computing.
Digging into Uncertainty in Self-supervised Multi-view Stereo
Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background.
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation
Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS).
JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
The key ideas are two-fold: a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints for better local feature representation learning; b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training.
Dynamic Group Convolution for Accelerating Convolutional Neural Networks
In this paper, we propose dynamic group convolution (DGC) that adaptively selects which part of input channels to be connected within each group for individual samples on the fly.
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally.
