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Found 29 papers, 19 papers with code
Semi-Supervised Unconstrained Head Pose Estimation in the Wild
Existing head pose estimation datasets are either composed of numerous samples by non-realistic synthesis or lab collection, or limited images by labor-intensive annotating.
Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training
The 2D human pose estimation (HPE) is a basic visual problem.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
BPJDet: Extended Object Representation for Generic Body-Part Joint Detection
In this paper, we focus on the joint detection of human body and its parts.
DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles
We present comprehensive comparisons with state-of-the-art single HPE methods on public benchmarks, as well as superior baseline results on our constructed MPHPE datasets.
On High dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization
Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize.
Body-Part Joint Detection and Association via Extended Object Representation
This paper focuses on the problem of joint detection of human body and its corresponding parts.
An Intuitive and Unconstrained 2D Cube Representation for Simultaneous Head Detection and Pose Estimation
Most recent head pose estimation (HPE) methods are dominated by the Euler angle representation.
Ranked #8 on
Head Pose Estimation
on BIWI
(MAE (trained with BIWI data) metric)
StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking
In this paper, we present StuArt, a novel automatic system designed for the individualized classroom observation, which empowers instructors to concern the learning status of each student.
SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection
Domain adaptive object detection (DAOD) aims to alleviate transfer performance degradation caused by the cross-domain discrepancy.
Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding
In the paper, we propose a single-stage end-to-end trainable framework for tackling the HBOE problem with multi-persons.
Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering
The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance.
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.
Music Source Separation with Generative Flow
Fully-supervised models for source separation are trained on parallel mixture-source data and are currently state-of-the-art.
Student Dangerous Behavior Detection in School
Video surveillance systems have been installed to ensure the student safety in schools.
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting
Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e. g. persons) in images.
High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning
The results show that, the proposed method can well suppress the noise and signal distortion caused by the laser frequency drift, laser phase noise, and interference fading, while recover the acoustic signals with high fidelity.
An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
Spoofing countermeasure (CM) systems are critical in speaker verification; they aim to discern spoofing attacks from bona fide speech trials.
Deoscillated Graph Collaborative Filtering
Stacking multiple cross-hop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers.
One-class learning towards generalized voice spoofing detection
Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion.
Y-Vector: Multiscale Waveform Encoder for Speaker Embedding
State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features.
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection
Quick detection of blade ice accretion is crucial for the maintenance of wind farms.
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes.
Bayesian Outdoor Defect Detection
We implement the Bayesian detector in the motion blurred drone images, in which the detector successfully identifies the hail damages on the rough surface and substantially enhances the accuracy of the entire defect detection pipeline.
Spectral Collaborative Filtering
Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF.
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination
Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning.
Graph Regularized Tensor Sparse Coding for Image Representation
Sparse coding (SC) is an unsupervised learning scheme that has received an increasing amount of interests in recent years.
Manifold regularization in structured output space for semi-supervised structured output prediction
Structured output prediction aims to learn a predictor to predict a structured output from a input data vector.
