Search Results for author:
Found 47 papers, 15 papers with code
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering
Compositional visual question answering requires reasoning over both semantic and geometry object relations.
MagicArticulate: Make Your 3D Models Articulation-Ready
With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation.
Prim2Room: Layout-Controllable Room Mesh Generation from Primitives
We propose Prim2Room, a novel framework for controllable room mesh generation leveraging 2D layout conditions and 3D primitive retrieval to facilitate precise 3D layout specification.
Gaussian Mixture based Evidential Learning for Stereo Matching
In this paper, we introduce a novel Gaussian mixture based evidential learning solution for robust stereo matching.
Text-to-Image Rectified Flow as Plug-and-Play Priors
Besides the generative capabilities of diffusion priors, motivated by the unique time-symmetry properties of rectified flow models, a variant of our method can additionally perform image inversion.
Sync4D: Video Guided Controllable Dynamics for Physics-Based 4D Generation
In this work, we introduce a novel approach for creating controllable dynamics in 3D-generated Gaussians using casually captured reference videos.
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN).
REACTO: Reconstructing Articulated Objects from a Single Video
In this paper, we address the challenge of reconstructing general articulated 3D objects from a single video.
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior
In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model.
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation.
Few-shot 3D Point Cloud Semantic Segmentation
Segmentation
+1
Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction
Lastly, to further improve the training process, FSS proposes a mesh thickness loss signal for pixel-aligned implicit models.
R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for 3D Clothed Human Digitalization
To this end we propose R-Cyclic Diffuser a framework that adapts Zero-1-to-3's novel approach to clothed human data by fusing it with a pixel-aligned implicit model.
Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting
We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks.
Few-shot Image Generation via Style Adaptation and Content Preservation
Many works propose to fine-tune a pre-trained GAN model.
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Recently, large-scale pre-trained models such as Segment-Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) have demonstrated remarkable success and revolutionized the field of computer vision.
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition
In this work, we tackle the challenging problem of long-tailed image recognition.
ELFNet: Evidential Local-global Fusion for Stereo Matching
Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation.
MoDA: Modeling Deformable 3D Objects from Casual Videos
To solve this problem, we propose neural dual quaternion blend skinning (NeuDBS) to achieve 3D point deformation, which can perform rigid transformation without skin-collapsing artifacts.
Effective End-to-End Vision Language Pretraining with Semantic Visual Loss
Current vision language pretraining models are dominated by methods using region visual features extracted from object detectors.
Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction
With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision.
CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks
Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images.
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas.
Ranked #23 on
3D Instance Segmentation
on ScanNet(v2)
Long-tailed Recognition by Learning from Latent Categories
Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training.
Ranked #9 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Self-Training Vision Language BERTs with a Unified Conditional Model
We use the labeled image data to train a teacher model and use the trained model to generate pseudo captions on unlabeled image data.
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph Propagation
We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
Ranked #1 on
3D Point Cloud Data Augmentation
on ModelNet40
On Representation Knowledge Distillation for Graph Neural Networks
Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings.
3D Pose Transfer with Correspondence Learning and Mesh Refinement
It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e. g., body shape) of the target mesh.
Few-Shot Segmentation with Global and Local Contrastive Learning
To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning.
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis
In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation.
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds
While dense labeling on 3D data is expensive and time-consuming, only a few works address weakly supervised semantic point cloud segmentation methods to relieve the labeling cost by learning from simpler and cheaper labels.
HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding
Scene flow in 3D point clouds plays an important role in understanding dynamic environments.
Feature Flow: In-network Feature Flow Estimation for Video Object Detection
Optical flow, which expresses pixel displacement, is widely used in many computer vision tasks to provide pixel-level motion information.
CRNet: Cross-Reference Networks for Few-Shot Segmentation
In this paper, we propose a cross-reference network (CRNet) for few-shot segmentation.
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and varying sizes.
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets.
Ranked #93 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Correlation Propagation Networks for Scene Text Detection
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).
Structured Learning of Tree Potentials in CRF for Image Segmentation
In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs.
Structured Learning of Binary Codes with Column Generation
Our column generation based method can be further generalized from the triplet loss to a general structured learning based framework that allows one to directly optimize multivariate performance measures.
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss
We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems.
CRF Learning with CNN Features for Image Segmentation
The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels.
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF.
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF.
Learning Deep Convolutional Features for MRI Based Alzheimer's Disease Classification
In this work, we propose to learn deep convolutional image features using unsupervised and supervised learning.
From Kernel Machines to Ensemble Learning
This finding not only enables us to design new ensemble learning methods directly from kernel methods, but also makes it possible to take advantage of those highly-optimized fast linear SVM solvers for ensemble learning.
Online Unsupervised Feature Learning for Visual Tracking
Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications.
Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification
In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement.
