Computer Vision
- Image Restoration/Enhancement:
Images are often subject to quality degradation such as blurring and noise. Using statistical models
of natural images, we have developed a series of methods to restore the degraded image to better
qualities.
Publications: [PR21], [ICCV15C], [CVPR15], [ICIP14], [ICIP14B], [CVPR14], [MANDM14B], [MANDM14A], [CVPR09], [CVPR08], [SPIE07], [PAMI09], [NIPS06]
- Object Tracking:
Tracking multiple moving objects in videos is an important task in computer vision and is an important part
of video analysis system. We have developed novel multi-object tracking methods based on hypergraphs.
Publications: [CVPR23], [ICIP22D], [CVIU21], [CVIU22], [CVIU20], [AAAI19A], [AIC18B], [AIC18A], [TIP17], [AVSS17], [AICITY17A], [AICITY17B], [OT17], [ICIP2017], [AAAI17], [TCYB17], [IJCV16], [TIP16], [OTITR15], [OT15], [PAMI16]
- Object Recognition/Detection: Object recognition and detection are the most important
and fundamental tasks in computer vision. We have developed several methods for object recognition/detection.
Publications: [CVPRW22], [BMVC21B], [AVSS21B], [AVSS21A], [CVIU21], [CVIU22], [ACMM20], [ECCV20A], [IJCNN20], [CVIU20], [ACMMM19], [AAAI19B], [ICPR18B], [ACCV12], [CVPR05A], [CVPR05B]
- Semantic Segmentation: Semantic segmentation is also known as scene parsing, which aims to label every pixel in an image to sematincally meaningful labels.
Publications: [TNNLS23], [JCB21A], [ICIP20], [AVSS19], [AVSS18]
- Image Annotation: Image annotation provides labels to objects in an image.
Publications: [IJCV18], [AAAI16], [ICCV15B], [PR15]
- Natural Image Statistics: Natural images are not randomly sampled pixels, and
there are regular statistical properties in them. We have developped several statistical models for natural images.
Publications: [NIPS10], [NC11], [NC09], [MM05], [NIPS08]
- Human Pose Estimation: Human pose estimation is an important task in computer vision.
It is the basis of human activity recognition and human centered robotic interaction.
Publications: [TIP22], [AAAI22A], [ECCV20B], [TCYB21], [ECCV18], [ICIP18], [BMVC15], [FG15]
- Adversarial Perturbation: Adversarial perturbations are specially designed patterned
noises that are added to images to disturb object detection algorithms.
Publications: [ICASSP24], [CVIU23], [ACML2023], [ICME23B], [ICME23A], [ICME22], [ICCV21B], [ICCV21A], [ICIP21A], [ICME21], [IJCNN20], [BMVC19], [BMVC18]
- Human Activity Recognition: Analyzing human activities in videos is important for many applications
in computer vision and video analysis.
Publications: [IROS20], [AAAI20], [ICASSP19B], [ICCV17], [ICCV15A]
- 3D Photometric Reconstruction: We can reconstruct 3D structures from a collection of digital images. The
Publications: [AVSS16], [ICIP15]
- Visual Saliency: Bottom up visual saliency is important to image retrieval and other computer vision tasks.
Publications: [TCVG16], [SPIE15], [ICCE15]
Machine Learning
- Learning Objectives: Objective function is a universal and important component in most machine learning systems. We have
studied new types of learning objectives.
Publications: [PAMI23], [JMLR22], [NEURIPS20], [PAMI20], [ACCESS20], [NIPS17], [NIPS12], [NIPS11], [UAI09]
- AUROC Maximization: Area under ROC curves (AUROC) is an important performance metric for binary classification, we study direct optimization
methods for AUROC.
Publications: [AISTATS16], [FRONTIER19], [UAI18], [ICML18], [NIPS16], [OPT15]
- Deep Learning: Deep learning is to use deep neural networks to solve machine learning problems.
Publications: [CVPR19], [IJCV19], [ACCV18], [CVPR18], [ICCV17], [IJCNN17], [ICCV15A], [ICANN14], [IJCAI13]
- Multi-label Learning: Multi-label learning is to assign an input data vector to one of many possible labels. Multi-label learning brings specific challenges to machine learning algorithms.
Publications: [ACCESS20], [IJCV18], [AAAI16], [ICCV15B], [PR15]
- Matrix Factorization: Matrix factorization is an important machine learning problem, which aims to decompose a matrix into the product of several simpler matrices.
Publications: [SPL19], [IJCAI19], [INFOSCI19], [PR18], [AAAI16], [ICLS14], [NIPSDDE13], [NIPS13], [ICASSP10A]
- Kernel Method: We designed new types of kernels for the task of object recognition.
Publications: [CVPR05A], [ECML05]
- Boosting: We developed new boosting methods based on the infomax principle.
Publications: [CVPR05B]