Search Results for author:
Found 53 papers, 16 papers with code
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.
DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation
Experimental results on $4, 773$ dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images
Fueled by the power of deep learning techniques and implicit shape learning, recent advances in single-image human digitalization have reached unprecedented accuracy and could recover fine-grained surface details such as garment wrinkles.
SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation
Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas.
Compound Domain Generalization via Meta-Knowledge Encoding
Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations.
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks.
Ranked #1 on
Video Frame Interpolation
on Vimeo90K
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e. g., coffee tables, writing desks).
Blind Image Super Resolution with Semantic-Aware Quantized Texture Prior
Since features in the codebook have shown the ability to generate natural textures in the pretrain stage, QuanTexSR can generate rich and realistic textures with the pretrained codebook as texture priors.
PointMatch: A Consistency Training Framework for Weakly SupervisedSemantic 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.
PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for Single-Image Novel View Synthesis
We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images, for novel view synthesis.
Pose2Room: Understanding 3D Scenes from Human Activities
To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment.
Revisiting Temporal Alignment for Video Restoration
Long-range temporal alignment is critical yet challenging for video restoration tasks.
Ranked #1 on
Video Super-Resolution
on Vimeo-90K
Pixel-level Intra-domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
SketchHairSalon: Deep Sketch-based Hair Image Synthesis
At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte.
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.
ME-PCN: Point Completion Conditioned on Mask Emptiness
Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.
SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design
In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of heads in character design.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition
In this paper, we analyze the difficulties of fine-grained image recognition from a new perspective and propose a transformer architecture with the peak suppression module and knowledge guidance module, which respects the diversification of discriminative features in a single image and the aggregation of discriminative clues among multiple images.
Ranked #6 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Image Recognition
Task-Aware Sampling Layer for Point-Wise Analysis
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds.
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset.
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.
Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images
Grounding referring expressions in RGBD image has been an emerging field.
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.
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition
Skeleton-based action recognition has attracted research attentions in recent years.
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
A deep learning based interactive sketching system for fashion images design
In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information.
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos
Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval.
Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
An indicator-guided learning mechanism is further proposed to ease the training of the proposed model.
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images
However, they all have their own drawbacks, and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topologicalstructures in their learning frameworks.
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc.
Learning Inverse Rendering of Faces from Real-world Videos
To alleviate this problem, we propose a weakly supervised training approach to train our model on real face videos, based on the assumption of consistency of albedo and normal across different frames, thus bridging the gap between real and synthetic face images.
Peeking into occluded joints: A novel framework for crowd pose estimation
Although occlusion widely exists in nature and remains a fundamental challenge for pose estimation, existing heatmap-based approaches suffer serious degradation on occlusions.
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation
Estimating 3D human pose from a single image is a challenging task.
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction.
Ranked #2 on
Room Layout Estimation
on SUN RGB-D
(using extra training data)
FPConv: Learning Local Flattening for Point Convolution
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.
Shallow2Deep: Indoor Scene Modeling by Single Image Understanding
Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling.
Self-Enhanced Convolutional Network for Facial Video Hallucination
It further recurrently exploits the reconstructed results and intermediate features of a sequence of preceding frames to improve the initial super-resolution of the current frame by modelling the coherence of structural facial features across frames.
HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation
Estimating 3D human pose from a single image is a challenging task.
Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks
The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification.
A Skeleton-bridged Deep Learning Approach for Generating Meshesof Complex Topologies from Single RGB Image
To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge.
A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images
To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge.
Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image
Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud.
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images
Due to high-resolution and small-size lesion regions, applying existing methods, such as U-Nets, to perform segmentation on fundus photography is very challenging.
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image.
CaricatureShop: Personalized and Photorealistic Caricature Sketching
To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed.
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information
Although significant advances have been made in the area of human poses estimation from images using deep Convolutional Neural Network (ConvNet), it remains a big challenge to perform 3D pose inference in-the-wild.
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
We present a new data-driven video inpainting method for recovering missing regions of video frames.
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features.
