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Found 12 papers, 7 papers with code
PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via Denoised Score Distillation
We first propose a novel score function, Denoised Score Distillation (DSD), which directly modifies the SDS by introducing negative gradient components to iteratively correct the gradient direction and generate high-quality textures.
Hi-ResNet: A High-Resolution Remote Sensing Network for Semantic Segmentation
In this work, we solve the above-mentioned problems by proposing a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs, which consists of a funnel module, a multi-branch module with stacks of information aggregation (IA) blocks, and a feature refinement module, sequentially, and Class-agnostic Edge Aware (CEA) loss.
Ranked #7 on
Semantic Segmentation
on LoveDA
CelebV-Text: A Large-Scale Facial Text-Video Dataset
This paper presents CelebV-Text, a large-scale, diverse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to-video generation tasks.
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
To solve this problem, we propose a rotation-invariant geometric relation to restore the relative pose with equivariant information for patches defined over different scales.
Rethinking Rotation Invariance with Point Cloud Registration
Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned.
Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding.
3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis
General point clouds have been increasingly investigated for different tasks, and recently Transformer-based networks are proposed for point cloud analysis.
Ranked #1 on
3D Point Cloud Classification
on IntrA
Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction
Recently, a series of deep learning based segmentation methods have been proposed to improve the quality of raw 3D optical image stacks by removing noises and restoring neuronal structures from low-contrast background.
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
Discrete point cloud objects lack sufficient shape descriptors of 3D geometries.
Ranked #10 on
3D Part Segmentation
on ShapeNet-Part
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis
Scene understanding is a critical problem in computer vision.
Ranked #4 on
3d scene graph generation
on 3DSSG
Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images
One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation.
ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis
Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition.
Generative Adversarial Network
Micro Expression Recognition
+1
