Search Results for author:
Found 54 papers, 23 papers with code
Automated Generation of Accurate & Fluent Medical X-ray Reports
Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists.
Generative Human Motion Stylization in Latent Space
Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code.
MotionScript: Natural Language Descriptions for Expressive 3D Human Motions
This paper proposes MotionScript, a motion-to-text conversion algorithm and natural language representation for human body motions.
MoMask: Generative Masked Modeling of 3D Human Motions
For the base-layer motion tokens, a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage.
Ranked #1 on
Motion Synthesis
on HumanML3D
Two-stage Synthetic Supervising and Multi-view Consistency Self-supervising based Animal 3D Reconstruction by Single Image
To address this challenge, we propose the combination of two-stage supervised and self-supervised training to address the challenge of obtaining animal cooperation for 3D scanning.
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B).
Event-based Human Pose Tracking by Spiking Spatiotemporal Transformer
Motivated by the above mentioned issues, we present in this paper a dedicated end-to-end sparse deep learning approach for event-based pose tracking: 1) to our knowledge this is the first time that 3D human pose tracking is obtained from events only, thus eliminating the need of accessing to any frame-based images as part of input; 2) our approach is based entirely upon the framework of Spiking Neural Networks (SNNs), which consists of Spike-Element-Wise (SEW) ResNet and a novel Spiking Spatiotemporal Transformer; 3) a large-scale synthetic dataset is constructed that features a broad and diverse set of annotated 3D human motions, as well as longer hours of event stream data, named SynEventHPD.
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
This gives rise to significantly more robust segmentation of image objects in complex scenes and under adverse conditions.
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet
In this paper, we propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage.
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
Our approach is flexible, could be used for both text2motion and motion2text tasks.
Ranked #2 on
Motion Captioning
on KIT Motion-Language
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
Dual Learning Music Composition and Dance Choreography
Music and dance have always co-existed as pillars of human activities, contributing immensely to the cultural, social, and entertainment functions in virtually all societies.
Generating Diverse and Natural 3D Human Motions From Text
Automated generation of 3D human motions from text is a challenging problem.
Ranked #6 on
Motion Synthesis
on InterHuman
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting.
Contrastive Learning for Unsupervised Video Highlight Detection
Our framework encodes a video into a vector representation by learning to pick video clips that help to distinguish it from other videos via a contrastive objective using dropout noise.
Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction
One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results.
Music-to-Dance Generation with Optimal Transport
We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music.
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.
3D Pose Estimation and Future Motion Prediction from 2D Images
This paper considers to jointly tackle the highly correlated tasks of estimating 3D human body poses and predicting future 3D motions from RGB image sequences.
The RETA Benchmark for Retinal Vascular Tree Analysis
Topological and geometrical analysis of retinal blood vessel is a cost-effective way for early detection of many common diseases.
Action2video: Generating Videos of Human 3D Actions
Action2motion stochastically generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.
Automated Generation of Accurate \& Fluent Medical X-ray Reports
Our paper focuses on automating the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists.
EventHPE: Event-based 3D Human Pose and Shape Estimation
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
Human Pose and Shape Estimation from Single Polarization Images
This paper focuses on a new problem of estimating human pose and shape from single polarization images.
Object Wake-up: 3D Object Rigging from a Single Image
A dataset of generic 3D objects with ground-truth annotated skeletons is collected.
Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds
However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences.
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search
A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results.
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Ranked #13 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Joint Visual and Audio Learning for Video Highlight Detection
In video highlight detection, the goal is to identify the interesting moments within an unedited video.
Reconstruct high-resolution multi-focal plane images from a single 2D wide field image
High-resolution 3D medical images are important for analysis and diagnosis, but axial scanning to acquire them is very time-consuming.
Action2Motion: Conditioned Generation of 3D Human Motions
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
3D Human Shape Reconstruction from a Polarization Image
Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images.
SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images
First, based on a generative human template, for every two frames having sufficient overlap, an initial pairwise alignment is performed; It is followed by a global non-rigid registration procedure, in which partial results from RGBD frames are collected into a unified 3D shape, under the guidance of correspondences from the pairwise alignment; Finally, the texture map of the reconstructed human model is optimized to deliver a clear and spatially consistent texture.
COMET: Context-Aware IoU-Guided Network for Small Object Tracking
To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.
Polarization Human Shape and Pose Dataset
Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent
Generative adversarial networks (GANs), famous for the capability of learning complex underlying data distribution, are however known to be tricky in the training process, which would probably result in mode collapse or performance deterioration.
Outlier Detection Ensemble with Embedded Feature Selection
Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance.
TBC-Net: A real-time detector for infrared small target detection using semantic constraint
Meanwhile, we propose a joint loss function and a training method.
Deep Learning for Visual Tracking: A Comprehensive Survey
Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized.
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN.
Estimating Position Bias without Intrusive Interventions
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal.
Offline Comparison of Ranking Functions using Randomized Data
In this paper, we study how to improve the data efficiency of IPS approaches in the offline comparison setting.
Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy
A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera.
Synthesizing Filamentary Structured Images with GANs
This paper aims at synthesizing filamentary structured images such as retinal fundus images and neuronal images, as follows: Given a ground-truth, to generate multiple realistic looking phantoms.
Multivariate Regression with Gross Errors on Manifold-valued Data
Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data.
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition
We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images.
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion.
Learning to Boost Filamentary Structure Segmentation
Step one of our approach centers on a data-driven latent classification tree model to detect the filamentary fragments.
Mouse Pose Estimation From Depth Images
We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images.
Transduction on Directed Graphs via Absorbing Random Walks
In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.
