Search Results for author:
Found 26 papers, 7 papers with code
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm
However, estimating multi-label noise transition matrices remains a challenging task, as most existing estimators in noisy multi-class learning rely on anchor points and accurate fitting of noisy class posteriors, which is hard to satisfy in noisy multi-label learning.
Self-Supervised Transformer with Domain Adaptive Reconstruction for General Face Forgery Video Detection
In this paper, a Self-supervised Transformer cooperating with Contrastive and Reconstruction learning (CoReST) is proposed, which is first pre-trained only on real face videos in a self-supervised manner, and then fine-tuned a linear head on specific face forgery video datasets.
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
We hence first model the mixture of noise patterns by all annotators, and then transfer this modeling to individual annotators.
Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation
In particular, we first train a model to fit the distribution of the training data and make it satisfy differential privacy by performing a randomized response mechanism during training process.
Deepfake Video Detection with Spatiotemporal Dropout Transformer
Existing image-level approaches often focus on single frame and ignore the spatiotemporal cues hidden in deepfake videos, resulting in poor generalization and robustness.
Bootstrapping Multi-view Representations for Fake News Detection
Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency.
Robust Weight Perturbation for Adversarial Training
Based on these observations, we propose a robust perturbation strategy to constrain the extent of weight perturbation.
Selective-Supervised Contrastive Learning with Noisy Labels
In the selection process, by measuring the agreement between learned representations and given labels, we first identify confident examples that are exploited to build confident pairs.
Ranked #11 on
Image Classification
on mini WebVision 1.0
Trustable Co-label Learning from Multiple Noisy Annotators
This raises the following important question: how can we effectively use a small amount of trusted data to facilitate robust classifier learning from multiple annotators?
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
Interpretable Face Manipulation Detection via Feature Whitening
The approach could make the face manipulation detection process transparent by embedding the feature whitening module.
Student Network Learning via Evolutionary Knowledge Distillation
Inspired by that, we propose an evolutionary knowledge distillation approach to improve the transfer effectiveness of teacher knowledge.
Receptive Multi-granularity Representation for Person Re-Identification
Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment.
Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network
Accurate temporal action proposals play an important role in detecting actions from untrimmed videos.
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition
A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints.
Ranked #30 on
Skeleton Based Action Recognition
on NTU RGB+D
Aesthetic Attributes Assessment of Images
This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency
In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model.
Ultrafast Video Attention Prediction with Coupled Knowledge Distillation
To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net.
Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively.
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).
Single Reference Image based Scene Relighting via Material Guided Filtering
We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer decomposition.
Privacy Preserving Face Retrieval in the Cloud for Mobile Users
The experimental results reveal that our protocol can successfully retrieve the proper photos from the cloud server and protect the user photos and the face detector.
Detecting Masked Faces in the Wild With LLE-CNNs
Detecting masked faces (i. e., faces with occlusions) is a challenging task due to two main reasons: 1)the absence of large datasets of masked faces, and 2)the absence of facial cues from the masked regions.
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
Only the base images are submitted randomly to the cloud server.
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation
Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.
