Search Results for author:
Found 23 papers, 9 papers with code
Neighborhood Contrastive Transformer for Change Captioning
Change captioning is to describe the semantic change between a pair of similar images in natural language.
Markerless Body Motion Capturing for 3D Character Animation based on Multi-view Cameras
This paper proposes a novel application system for the generation of three-dimensional (3D) character animation driven by markerless human body motion capturing.
Spatial-Temporal Pyramid Graph Reasoning for Action Recognition
Spatial-temporal relation reasoning is a significant yet challenging problem for video action recognition.
Ranked #33 on
Action Recognition
on Something-Something V1
G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
To go further, in this paper, we propose a unified Gaussian representation called G-Rep to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems.
Distinguishing Unseen From Seen for Generalized Zero-Shot Learning
In this paper, we present a novel method which leverages both visual and semantic modalities to distinguish seen and unseen categories.
TransZero: Attribute-guided Transformer for Zero-Shot Learning
Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.
FEAFA+: An Extended Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
Extended FEAFA (FEAFA+) includes 150 video sequences from FEAFA and DISFA, with a total of 230, 184 frames being manually annotated on floating-point intensity value of 24 redefined AUs using the Expression Quantitative Tool.
Domain Adaptive Semantic Segmentation without Source Data
To address these problems, we investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain, and then adapting to the target domain without accessing source data anymore.
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis.
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers.
Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network
Chinese is one of the most widely used languages in the world, yet online handwritten Chinese character recognition (OLHCCR) remains challenging.
Cycle-consistent Conditional Adversarial Transfer Networks
Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions.
Ranked #3 on
Domain Adaptation
on USPS-to-MNIST
Alleviating Feature Confusion for Generative Zero-shot Learning
An inevitable issue of such a paradigm is that the synthesized unseen features are prone to seen references and incapable to reflect the novelty and diversity of real unseen instances.
Agile Domain Adaptation
Because of the distribution shifts, different target samples have distinct degrees of difficulty in adaptation.
From Zero-Shot Learning to Cold-Start Recommendation
This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR.
Leveraging the Invariant Side of Generative Zero-Shot Learning
In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions.
Ranked #4 on
Generalized Zero-Shot Learning
on SUN Attribute
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation.
Sample-Efficient Policy Learning based on Completely Behavior Cloning
Direct policy search is one of the most important algorithm of reinforcement learning.
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams.
Learning Correspondence Structures for Person Re-identification
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
Challenge of Multi-Camera Tracking
Multi-camera tracking is quite different from single camera tracking, and it faces new technology and system architecture challenges.
Multiview Hessian regularized logistic regression for action recognition
With the rapid development of social media sharing, people often need to manage the growing volume of multimedia data such as large scale video classification and annotation, especially to organize those videos containing human activities.
Binary Code Ranking with Weighted Hamming Distance
In this paper, we propose a weighted Hamming distance ranking algorithm (WhRank) to rank the binary codes of hashing methods.
