Search Results for author:
Found 21 papers, 6 papers with code
Age Prediction From Face Images Via Contrastive Learning
This paper presents a novel approach for accurately estimating age from face images, which overcomes the challenge of collecting a large dataset of individuals with the same identity at different ages.
LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement
In this paper, we address this limitation by proposing a degradation-aware learning scheme for LLIE using diffusion models, which effectively integrates degradation and image priors into the diffusion process, resulting in improved image enhancement.
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Second, we introduce a residual dense transformer block (RDTB) as the final GridFormer layer.
UserBERT: Modeling Long- and Short-Term User Preferences via Self-Supervision
This paper extends the BERT model to e-commerce user data for pre-training representations in a self-supervised manner.
Deep Image Deblurring: A Survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image.
MC-Blur: A Comprehensive Benchmark for Image Deblurring
Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios.
Facial Action Unit Detection With Transformers
The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis.
UserBERT: Self-supervised User Representation Learning
This paper extends the BERT model to user data for pretraining user representations in a self-supervised way.
Deblurring by Realistic Blurring
To address this problem, we propose a new method which combines two GAN models, i. e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
Ranked #17 on
Deblurring
on HIDE (trained on GOPRO)
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model.
Deep Heterogeneous Autoencoders for Collaborative Filtering
This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems.
RGB-based 3D Hand Pose Estimation via Privileged Learning with Depth Images
We explore different ways of using this privileged information: (1) using depth data to initially train a depth-based network, (2) using the features from the depth-based network of the paired depth images to constrain mid-level RGB network weights, and (3) using the foreground mask, obtained from the depth data, to suppress the responses from the background area.
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Ranked #5 on
Hand Pose Estimation
on HANDS 2017
BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
We also show significant improvements in egocentric hand pose estimation with a CNN trained on the new dataset.
Pano2CAD: Room Layout From A Single Panorama Image
This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image.
Human Body Shape Estimation Using a Multi-Resolution Manifold Forest
This paper proposes a method for estimating the 3D body shape of a person with robustness to clothing.
Bi-label Propagation for Generic Multiple Object Tracking
In this paper, we propose a label propagation framework to handle the multiple object tracking (MOT) problem for a generic object type (cf.
Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
In this paper we introduce a new distance for robustly matching vectors of 3D rotations.
Expressive Visual Text-to-Speech Using Active Appearance Models
This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights.
