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Found 58 papers, 18 papers with code
A Practice of Tourism Knowledge Graph Construction based on Heterogeneous Information
So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively.
Binarized Neural Network for Single Image Super Resolution
Lighter model and faster inference are the focus of current single image super-resolution (SISR) research.
Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement
Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems.
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID
To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters.
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval
Zero-shot sketch-based image retrieval (ZS-SBIR) is challenging due to the cross-domain nature of sketches and photos, as well as the semantic gap between seen and unseen image distributions.
Semantic-aware Generation of Multi-view Portrait Drawings
Our motivation is that facial semantic labels are view-consistent and correlate with drawing techniques.
Boosting Weakly-Supervised Temporal Action Localization with Text Information
For the discriminative objective, we propose a Text-Segment Mining (TSM) mechanism, which constructs a text description based on the action class label, and regards the text as the query to mine all class-related segments.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint
The proposed Bi-SCC firstly adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video; Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Masked and Adaptive Transformer for Exemplar Based Image Translation
To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand.
Few-shot Face Image Translation via GAN Prior Distillation
GPD contains two models: a teacher network with GAN Prior and a student network that fulfills end-to-end translation.
Few-shot Font Generation by Learning Style Difference and Similarity
To address this issue, we propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font).
Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues
Face forgery detection plays an important role in personal privacy and social security.
All-to-key Attention for Arbitrary Style Transfer
In this paper, we propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features -- that is more in line with the characteristics of style transfer.
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours'.
VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild
Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and (3) face enhancement for improving photo-realism.
NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction
In this paper, we propose a neural architecture representation model that can be used to estimate these attributes holistically.
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization Feature Space
We show that the cycle-consistency regularization helps to minimize the volume of the transition matrix T indirectly without exploiting the estimated noisy class posterior, which could further encourage the estimated transition matrix T to converge to its optimal solution.
FedForgery: Generalized Face Forgery Detection with Residual Federated Learning
Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery.
Strength-Adaptive Adversarial Training
Firstly, applying a pre-specified perturbation budget on networks of various model capacities will yield divergent degree of robustness disparity between natural and robust accuracies, which deviates from robust network's desideratum.
Visual Information Hiding Based on Obfuscating Adversarial Perturbations
Growing leakage and misuse of visual information raise security and privacy concerns, which promotes the development of information protection.
Improving Adversarial Robustness via Mutual Information Estimation
To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method.
TransFA: Transformer-based Representation for Face Attribute Evaluation
The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning.
Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario
In recent years, with the rapid development of face editing and generation, more and more fake videos are circulating on social media, which has caused extreme public concerns.
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers.
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios.
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
In this paper, we learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition.
Semi-parametric Makeup Transfer via Semantic-aware Correspondence
The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer.
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id
However, since person Re-Id is an open-set problem, the clustering based methods often leave out lots of outlier instances or group the instances into the wrong clusters, thus they can not make full use of the training samples as a whole.
Modeling Adversarial Noise for Adversarial Defense
Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks.
Single Image Dehazing with An Independent Detail-Recovery Network
In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.
Modeling Adversarial Noise for Adversarial Training
Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks.
Support-Set Based Cross-Supervision for Video Grounding
The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.
Exploring Set Similarity for Dense Self-supervised Representation Learning
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.
Kernel Mean Estimation by Marginalized Corrupted Distributions
Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms.
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
However, pre-processing methods may suffer from the robustness degradation effect, in which the defense reduces rather than improving the adversarial robustness of a target model in a white-box setting.
Towards Defending against Adversarial Examples via Attack-Invariant Features
However, given the continuously evolving attacks, models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples.
Removing Adversarial Noise in Class Activation Feature Space
Then, we train a denoising model to minimize the distances between the adversarial examples and the natural examples in the class activation feature space.
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).
Syncretic Modality Collaborative Learning for Visible Infrared Person Re-Identification
Visible infrared person re-identification (VI-REID) aims to match pedestrian images between the daytime visible and nighttime infrared camera views.
Robust early-learning: Hindering the memorization of noisy labels
The \textit{early stopping} method therefore can be exploited for learning with noisy labels.
Ranked #32 on
Image Classification
on mini WebVision 1.0
(ImageNet Top-1 Accuracy metric)
ADD-Defense: Towards Defending Widespread Adversarial Examples via Perturbation-Invariant Representation
Motivated by this observation, we propose a defense framework ADD-Defense, which extracts the invariant information called \textit{perturbation-invariant representation} (PIR) to defend against widespread adversarial examples.
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
The traditional transition matrix is limited to model closed-set label noise, where noisy training data has true class labels within the noisy label set.
Class2Simi: A New Perspective on Learning with Label Noise
It is worthwhile to perform the transformation: We prove that the noise rate for the noisy similarity labels is lower than that of the noisy class labels, because similarity labels themselves are robust to noise.
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles
To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles.
Weakly Supervised Temporal Action Localization with Segment-Level Labels
Specifically, we devise a partial segment loss regarded as a loss sampling to learn integral action parts from labeled segments.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
To give an affirmative answer, in this paper, we propose a framework called Class2Simi: it transforms data points with noisy class labels to data pairs with noisy similarity labels, where a similarity label denotes whether a pair shares the class label or not.
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.
Facial Attribute Capsules for Noise Face Super Resolution
In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs.
Multi-Class Classification from Noisy-Similarity-Labeled Data
We further estimate the transition matrix from only noisy data and build a novel learning system to learn a classifier which can assign noise-free class labels for instances.
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames.
Are Anchor Points Really Indispensable in Label-Noise Learning?
Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i. e., data points that belong to a specific class almost surely).
Ranked #17 on
Learning with noisy labels
on CIFAR-10N-Random3
Saliency deep embedding for aurora image search
In this paper, we aim to explore a new search method for images captured with circular fisheye lens, especially the aurora images.
Random Sampling for Fast Face Sketch Synthesis
The most time-consuming or main computation complexity for exemplar-based face sketch synthesis methods lies in the neighbor selection process.
Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors.
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics.
Graphical Representation for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i. e., different sensors or different wavelengths) for identification.
Facial Feature Point Detection: A Comprehensive Survey
CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point.
