Search Results for author:
Found 30 papers, 8 papers with code
Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation
In this paper, we propose a Weakly supervised Iterative Spinal Segmentation (WISS) method leveraging only four corner landmark weak labels on a single sagittal slice to achieve automatic volumetric segmentation from CT images for VBs.
Learning-based Bone Quality Classification Method for Spinal Metastasis
In this paper, we explore a learning-based automatic bone quality classification method for spinal metastasis based on CT images.
ClothPose: A Real-world Benchmark for Visual Analysis of Garment Pose via An Indirect Recording Solution
In this work, we propose a recording system, GarmentTwin, which can track garment poses in dynamic settings such as manipulation.
Registration based Few-Shot Anomaly Detection
Inspired by how humans detect anomalies, i. e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model.
Ranked #89 on
Anomaly Detection
on MVTec AD
Task Decoupled Framework for Reference-Based Super-Resolution
Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.
LAR-SR: A Local Autoregressive Model for Image Super-Resolution
Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output.
CaT: Weakly Supervised Object Detection with Category Transfer
To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets.
ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass
In addition, we specifically select a subset from COCO val2017 named COCO ContourHard-val to further demonstrate the contour quality improvements.
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided.
Ranked #27 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit
Meanwhile, human choreographers design dance motions from music in a two-stage manner: they firstly devise multiple choreographic dance units (CAUs), each with a series of dance motions, and then arrange the CAU sequence according to the rhythm, melody and emotion of the music.
Decoupled Variational Embedding for Signed Directed Networks
Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE.
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph
Most existing methods focus on preserving the first-order proximity between entities in the KG.
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.
Bottom-Up Temporal Action Localization with Mutual Regularization
To alleviate this problem, we introduce two regularization terms to mutually regularize the learning procedure: the Intra-phase Consistency (IntraC) regularization is proposed to make the predictions verified inside each phase; and the Inter-phase Consistency (InterC) regularization is proposed to keep consistency between these phases.
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning
We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL).
Cascading: Association Augmented Sequential Recommendation
Recently, recommendation according to sequential user behaviors has shown promising results in many application scenarios.
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
For the backbone, we propose multi-branch multi-scale graph convolution networks to extract spatial and temporal features.
Ranked #41 on
Skeleton Based Action Recognition
on NTU RGB+D
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks.
Handwritten Chinese Font Generation with Collaborative Stroke Refinement
Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters.
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics.
Accelerate CNN via Recursive Bayesian Pruning
A new dropout-based measurement of redundancy, which facilitate the computation of posterior assuming inter-layer dependency, is introduced.
Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption
Unsupervised domain adaption aims to learn a powerful classifier for the target domain given a labeled source data set and an unlabeled target data set.
Phase Collaborative Network for Two-Phase Medical Image Segmentation
However, in medical image analysis, fusing prediction from two phases is often difficult, because (i) there is a domain gap between two phases, and (ii) the semantic labels are not pixel-wise corresponded even for images scanned from the same patient.
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results.
Ranked #29 on
Human-Object Interaction Detection
on V-COCO
Modality Attention for End-to-End Audio-visual Speech Recognition
In this paper, we propose a novel multimodal attention based method for audio-visual speech recognition which could automatically learn the fused representation from both modalities based on their importance.
Audio-Visual Speech Recognition
Robust Speech Recognition
+2
An Element Sensitive Saliency Model with Position Prior Learning for Web Pages
Understanding human visual attention is important for multimedia applications.
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on
3D Face Reconstruction
on Florence
Deep Hashing with Triplet Quantization Loss
While minimizing the quantization loss guarantees that quantization has minimal effect on retrieval accuracy, it unfortunately significantly reduces the expressiveness of features even before the quantization.
