Search Results for author: Qiang Ji

Found 53 papers, 5 papers with code

Epistemic Uncertainty Quantification For Pre-trained Neural Network

no code implementations15 Apr 2024 Hanjing Wang, Qiang Ji

Specifically, we propose a gradient-based approach to assess epistemic uncertainty, analyzing the gradients of outputs relative to model parameters, and thereby indicating necessary model adjustments to accurately represent the inputs.

Active Learning Out-of-Distribution Detection +1

Epistemic Uncertainty Quantification For Pre-Trained Neural Networks

no code implementations CVPR 2024 Hanjing Wang, Qiang Ji

Specifically we propose a gradient-based approach to assess epistemic uncertainty analyzing the gradients of outputs relative to model parameters and thereby indicating necessary model adjustments to accurately represent the inputs.

Active Learning Out-of-Distribution Detection +1

Uncertainty-aware Action Decoupling Transformer for Action Anticipation

no code implementations CVPR 2024 Hongji Guo, Nakul Agarwal, Shao-Yuan Lo, Kwonjoon Lee, Qiang Ji

The objective is to make the two decoupled tasks assist each other and eventually improve the action anticipation task.

Action Anticipation

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model

1 code implementation20 Dec 2023 Naiyu Yin, Tian Gao, Yue Yu, Qiang Ji

We then propose an effective two-phase iterative DAG learning algorithm to address the increasing optimization difficulties and to learn a causal DAG from data with heteroscedastic variable noise under varying variance.

Causal Discovery

Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body Reconstruction

no code implementations ICCV 2023 Yufei Zhang, Hanjing Wang, Jeffrey O. Kephart, Qiang Ji

While 3D body reconstruction methods have made remarkable progress recently, it remains difficult to acquire the sufficiently accurate and numerous 3D supervisions required for training.

3D Reconstruction

Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning

no code implementations CVPR 2023 Hanjing Wang, Dhiraj Joshi, Shiqiang Wang, Qiang Ji

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs.

Uncertainty Quantification

Physics-Augmented Autoencoder for 3D Skeleton-Based Gait Recognition

no code implementations ICCV 2023 Hongji Guo, Qiang Ji

During the inference, the decoder is discared and a RNN-based classifier takes the output of the encoder for gait recognition.

Decoder Gait Recognition

Biomechanics-Guided Facial Action Unit Detection Through Force Modeling

no code implementations CVPR 2023 Zijun Cui, Chenyi Kuang, Tian Gao, Kartik Talamadupula, Qiang Ji

In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces are modelled, and are employed to predict AU activation.

Action Unit Detection Facial Action Unit Detection

Knowledge-augmented Deep Learning and Its Applications: A Survey

no code implementations30 Nov 2022 Zijun Cui, Tian Gao, Kartik Talamadupula, Qiang Ji

Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge.

Probabilistic Debiasing of Scene Graphs

1 code implementation CVPR 2023 Bashirul Azam Biswas, Qiang Ji

The quality of scene graphs generated by the state-of-the-art (SOTA) models is compromised due to the long-tail nature of the relationships and their parent object pairs.


Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data

no code implementations16 Jun 2022 Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji

Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications.

Causal Discovery

Automatic Gaze Analysis: A Survey of Deep Learning based Approaches

1 code implementation12 Aug 2021 Shreya Ghosh, Abhinav Dhall, Munawar Hayat, Jarrod Knibbe, Qiang Ji

Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction.

Gaze Estimation

Bayesian Eye Tracking

no code implementations25 Jun 2021 Qiang Ji, Kang Wang

Model-based eye tracking, however, is susceptible to eye feature detection errors, in particular for eye tracking in the wild.

Bayesian Inference Gaze Estimation

Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation

no code implementations CVPR 2021 Tengfei Song, Zijun Cui, Yuru Wang, Wenming Zheng, Qiang Ji

Second, we introduce probabilistic graph convolution that allows to perform graph convolution on the distribution of Bayesian Network structure to extract AU structural features.

Hybrid Message Passing With Performance-Driven Structures for Facial Action Unit Detection

no code implementations CVPR 2021 Tengfei Song, Zijun Cui, Wenming Zheng, Qiang Ji

In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner.

Action Unit Detection Facial Action Unit Detection

DAGs with No Curl: An Efficient DAG Structure Learning Approach

1 code implementation14 Jun 2021 Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji

To further improve efficiency, we propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly.

Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition

no code implementations NeurIPS 2020 Zijun Cui, Tengfei Song, Yuru Wang, Qiang Ji

This paper proposes to systematically capture their dependencies and incorporate them into a deep learning framework for joint facial expression recognition and action unit detection.

Action Unit Detection Anatomy +2

Type-augmented Relation Prediction in Knowledge Graphs

no code implementations16 Sep 2020 Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji

Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones.

Relation Vocal Bursts Type Prediction

Copula-based local dependence between energy, agriculture and metal commodity markets

no code implementations9 Mar 2020 Claudiu Albulescu, Aviral Tiwari, Qiang Ji

In all pairs of commodity indexes, we find increased co-movements in extreme situations, a stronger dependence between energy and other commodity markets at lower tails, and a 'V-type' local dependence for the energy-metal pairs.


Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey

no code implementations3 Oct 2019 Sicheng Zhao, Shangfei Wang, Mohammad Soleymani, Dhiraj Joshi, Qiang Ji

Affective computing (AC) of these data can help to understand human behaviors and enable wide applications.

Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications

no code implementations12 Mar 2019 Chao Gou, Tianyu Shen, Wenbo Zheng, Huadan Xue, Hui Yu, Qiang Ji, Zhengyu Jin, Fei-Yue Wang

Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge.

Classifier Learning With Prior Probabilities for Facial Action Unit Recognition

no code implementations CVPR 2018 Yong Zhang, Wei-Ming Dong, Bao-Gang Hu, Qiang Ji

To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities.

Anatomy Facial Action Unit Detection

Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation

no code implementations CVPR 2018 Yong Zhang, Wei-Ming Dong, Bao-Gang Hu, Qiang Ji

Facial action unit (AU) intensity estimation plays an important role in affective computing and human-computer interaction.

Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation

no code implementations CVPR 2018 Yong Zhang, Rui Zhao, Wei-Ming Dong, Bao-Gang Hu, Qiang Ji

The majority of methods directly apply supervised learning techniques to AU intensity estimation while few methods exploit unlabeled samples to improve the performance.


Facial Landmark Detection: a Literature Survey

no code implementations15 May 2018 Yue Wu, Qiang Ji

The regression-based methods implicitly capture facial shape and appearance information.

Facial Landmark Detection regression

Deep Regression Bayesian Network and Its Applications

no code implementations13 Oct 2017 Siqi Nie, Meng Zheng, Qiang Ji

The major difficulty of learning and inference with deep directed models with many latent variables is the intractable inference due to the dependencies among the latent variables and the exponential number of latent variable configurations.


Real Time Eye Gaze Tracking With 3D Deformable Eye-Face Model

no code implementations ICCV 2017 Kang Wang, Qiang Ji

The key idea is to leverage on the proposed 3D eye-face model, from which we can estimate 3D eye gaze from observed 2D facial landmarks.

Face Model Gaze Estimation

A Multimodal Deep Regression Bayesian Network for Affective Video Content Analyses

no code implementations ICCV 2017 Quan Gan, Shangfei Wang, Longfei Hao, Qiang Ji

After that, a joint representation is extracted from the top layers of the two deep networks, and thus captures the high order dependencies between visual modality and audio modality.


Deep Facial Action Unit Recognition From Partially Labeled Data

no code implementations ICCV 2017 Shan Wu, Shangfei Wang, Bowen Pan, Qiang Ji

To address this, we propose a deep facial action unit recognition approach learning from partially AU-labeled data.

Facial Action Unit Detection

Constrained Deep Transfer Feature Learning and its Applications

no code implementations CVPR 2016 Yue Wu, Qiang Ji

Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as a constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain.

Facial Expression Recognition Facial Expression Recognition (FER) +1

Robust Facial Landmark Detection under Significant Head Poses and Occlusion

no code implementations ICCV 2015 Yue Wu, Qiang Ji

In this work, we propose a unified robust cascade regression framework that can handle both images with severe occlusion and images with large head poses.

Facial Landmark Detection Occlusion Estimation +1

Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion

no code implementations CVPR 2017 Yue Wu, Chao Gou, Qiang Ji

Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision.

Facial Landmark Detection Head Pose Estimation

Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection

no code implementations CVPR 2016 Yue Wu, Qiang Ji

Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection.

Facial Action Unit Detection Facial Landmark Detection +1

A Hierarchical Probabilistic Model for Facial Feature Detection

no code implementations CVPR 2014 Yue Wu, Ziheng Wang, Qiang Ji

Facial feature detection from facial images has attracted great attention in the field of computer vision.

Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines

no code implementations CVPR 2013 Yue Wu, Zuoguan Wang, Qiang Ji

To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes.

Structured Feature Selection

no code implementations ICCV 2015 Tian Gao, Ziheng Wang, Qiang Ji

Then we apply structured feature selection to two applications: 1) We introduce a new method that enables STMB to scale up and show the competitive performance of our algorithms on large-scale image classification tasks.

Dimensionality Reduction feature selection +2

Local Causal Discovery of Direct Causes and Effects

no code implementations NeurIPS 2015 Tian Gao, Qiang Ji

We focus on the discovery and identification of direct causes and effects of a target variable in a causal network.

Causal Discovery

Latent Regression Bayesian Network for Data Representation

no code implementations15 Jun 2015 Siqi Nie, Qiang Ji

Deep directed generative models have attracted much attention recently due to their expressive representation power and the ability of ancestral sampling.


Classifier Learning With Hidden Information

no code implementations CVPR 2015 Ziheng Wang, Qiang Ji

Experimental results on different applications demonstrate the effectiveness of the proposed methods for exploiting hidden information and their superior performance to existing methods.

Video Event Recognition With Deep Hierarchical Context Model

no code implementations CVPR 2015 Xiaoyang Wang, Qiang Ji

Video event recognition still faces great challenges due to large intra-class variation and low image resolution, in particular for surveillance videos.

Advances in Learning Bayesian Networks of Bounded Treewidth

no code implementations NeurIPS 2014 Siqi Nie, Denis Deratani Maua, Cassio Polpo de Campos, Qiang Ji

This work presents novel algorithms for learning Bayesian network structures with bounded treewidth.

Constrained Clustering and Its Application to Face Clustering in Videos

no code implementations CVPR 2013 Baoyuan Wu, Yifan Zhang, Bao-Gang Hu, Qiang Ji

As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks.

Constrained Clustering Face Clustering

Learning with Target Prior

no code implementations NeurIPS 2012 Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji

In this work, we describe a new learning scheme for parametric learning, in which the target variables $\y$ can be modeled with a prior model $p(\y)$ and the relations between data and target variables are estimated through $p(\y)$ and a set of uncorresponded data $\x$ in training.

Pose Estimation

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