Search Results for author:
Found 111 papers, 41 papers with code
Real Face Foundation Representation Learning for Generalized Deepfake Detection
In this study, we propose Real Face Foundation Representation Learning (RFFR), which aims to learn a general representation from large-scale real face datasets and detect potential artifacts outside the distribution of RFFR.
Diversity-Measurable Anomaly Detection
In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies.
Ranked #1 on
Anomaly Detection
on UCSD Ped2
Anomaly Detection In Surveillance Videos
Defect Detection
+1
Hierarchical Compositional Representations for Few-shot Action Recognition
Recently action recognition has received more and more attention for its comprehensive and practical applications in intelligent surveillance and human-computer interaction.
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation
Firstly, we introduce a class-aware cross entropy (CCE) loss for network training.
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild
To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions.
UniCon+: ICTCAS-UCAS Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2022
This report presents a brief description of our winning solution to the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2022.
Clothes-Changing Person Re-identification with RGB Modality Only
In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w. r. t.
Ranked #2 on
Person Re-Identification
on PRCC
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability.
Ranked #2 on
Visual Object Tracking
on LaSOT-ext
Enhancing Face Recognition With Self-Supervised 3D Reconstruction
In this paper, we propose to enhance face recognition with a bypass of self-supervised 3D reconstruction, which enforces the neural backbone to focus on the identity-related depth and albedo information while neglects the identity-irrelevant pose and illumination information.
Adaptive Image Transformations for Transfer-based Adversarial Attack
Adversarial attacks provide a good way to study the robustness of deep learning models.
Adaptive Perturbation for Adversarial Attack
Instead of using the sign function, we propose to directly utilize the exact gradient direction with a scaling factor for generating adversarial perturbations, which improves the attack success rates of adversarial examples even with fewer perturbations.
Learning Fair Face Representation With Progressive Cross Transformer
To mitigate racial bias and meantime preserve robust FR, we abstract face identity-related representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition.
Meta Gradient Adversarial Attack
In recent years, research on adversarial attacks has become a hot spot.
UniCon: Unified Context Network for Robust Active Speaker Detection
Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties.
Locality-aware Channel-wise Dropout for Occluded Face Recognition
Furthermore, by randomly dropping out several feature channels, our method can well simulate the occlusion of larger area.
Graph Jigsaw Learning for Cartoon Face Recognition
To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner.
Gaze Estimation with an Ensemble of Four Architectures
This paper presents a method for gaze estimation according to face images.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Feature Completion for Occluded Person Re-Identification
Our method significantly outperforms existing methods on the occlusion datasets, while remains top even superior performance on holistic datasets.
ICTCAS-UCAS-TAL Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2021
This report presents a brief description of our method for the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2021.
Meta Auxiliary Learning for Facial Action Unit Detection
The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties.
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification
Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts.
EigenGAN: Layer-Wise Eigen-Learning for GANs
Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations.
Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking
This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN), for visual object tracking.
Cross-Encoder for Unsupervised Gaze Representation Learning
To address the issue that the feature of gaze is always intertwined with the appearance of the eye, Cross-Encoder disentangles the features using a latent-code-swapping mechanism on eye-consistent image pairs and gaze-similar ones.
Attributes Aware Face Generation with Generative Adversarial Networks
Recent studies have shown remarkable success in face image generations.
Learn an Effective Lip Reading Model without Pains
Considering the non-negligible effects of these strategies and the existing tough status to train an effective lip reading model, we perform a comprehensive quantitative study and comparative analysis, for the first time, to show the effects of several different choices for lip reading.
Ranked #1 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
(using extra training data)
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification
Furthermore, a Channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts.
Temporal Complementary Learning for Video Person Re-Identification
This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification.
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation
Scene graph aims to faithfully reveal humans' perception of image content.
Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling
Remote physiological measurements, e. g., remote photoplethysmography (rPPG) based heart rate (HR), heart rate variability (HRV) and respiration frequency (RF) measuring, are playing more and more important roles under the application scenarios where contact measurement is inconvenient or impossible.
PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing
Facial attribute editing aims to manipulate attributes on the human face, e. g., adding a mustache or changing the hair color.
Synchronous Bidirectional Learning for Multilingual Lip Reading
Based on this idea, we try to explore the synergized learning of multilingual lip reading in this paper, and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Single-Side Domain Generalization for Face Anti-Spoofing
In this work, we propose an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing.
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning
In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD.
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text
Then, we introduce three aggregators which guide the message passing from one graph to another to utilize the contexts in various modalities, so as to refine the features of nodes.
The 1st Challenge on Remote Physiological Signal Sensing (RePSS)
Remote measurement of physiological signals from videos is an emerging topic.
Mutual Information Maximization for Effective Lip Reading
By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading.
Ranked #8 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Deformation Flow Based Two-Stream Network for Lip Reading
Observing on the continuity in adjacent frames in the speaking process, and the consistency of the motion patterns among different speakers when they pronounce the same phoneme, we model the lip movements in the speaking process as a sequence of apparent deformations in the lip region.
Ranked #6 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading
On the one hand, we introduce the evaluation metric (refers to the character error rate in this paper) as a form of reward to optimize the model together with the original discriminative target.
Ranked #9 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition
Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR).
Ranked #2 on
Lipreading
on GRID corpus (mixed-speech)
Emotion Recognition for In-the-wild Videos
This paper is a brief introduction to our submission to the seven basic expression classification track of Affective Behavior Analysis in-the-wild Competition held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020.
$M^3$T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild
This report describes a multi-modal multi-task ($M^3$T) approach underlying our submission to the valence-arousal estimation track of the Affective Behavior Analysis in-the-wild (ABAW) Challenge, held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020.
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
Combined variations containing low-resolution and occlusion often present in face images in the wild, e. g., under the scenario of video surveillance.
Deep Heterogeneous Hashing for Face Video Retrieval
To tackle the key challenge of hashing on the manifold, a well-studied Riemannian kernel mapping is employed to project data (i. e. covariance matrices) into Euclidean space and thus enables to embed the two heterogeneous representations into a common Hamming space, where both intra-space discriminability and inter-space compatibility are considered.
RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation
Recently, some methods have been proposed for remote HR estimation from face videos; however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e. g., with head movement, and bad illumination) are not known.
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
In this work, we propose a semi-supervised approach for AU recognition utilizing a large number of web face images without AU labels and a relatively small face dataset with AU annotations inspired by the co-training methods.
Cross Attention Network for Few-shot Classification
The unseen classes and low-data problem make few-shot classification very challenging.
Cross-modal Scene Graph Matching for Relationship-aware Image-Text Retrieval
In the light of recent success of scene graph in many CV and NLP tasks for describing complex natural scenes, we propose to represent image and text with two kinds of scene graphs: visual scene graph (VSG) and textual scene graph (TSG), each of which is exploited to jointly characterize objects and relationships in the corresponding modality.
Hierarchical Disentangle Network for Object Representation Learning
In this paper, we propose the hierarchical disentangle network (HDN) to exploit the rich hierarchical characteristics among categories to divide the disentangling process in a coarse-to-fine manner, such that each level only focuses on learning the specific representations in its granularity and finally the common and unique representations in all granularities jointly constitute the raw object.
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Transferable Contrastive Network for Generalized Zero-Shot Learning
Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes.
Ranked #5 on
Zero-Shot Learning
on SUN Attribute
Temporal Knowledge Propagation for Image-to-Video Person Re-identification
With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated.
Ranked #6 on
Person Re-Identification
on iLIDS-VID
Image-To-Video Person Re-Identification
Video-Based Person Re-Identification
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense
To comprehensively evaluate such abilities, we propose a VQA benchmark, CRIC, which introduces new types of questions about Compositional Reasoning on vIsion and Commonsense, and an evaluation metric integrating the correctness of answering and commonsense grounding.
VRSTC: Occlusion-Free Video Person Re-Identification
For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame.
Interaction-and-Aggregation Network for Person Re-identification
Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings.
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection
Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors.
Multi-Task Learning for Audio Visual Active Speaker Detection
This report describes the approach underlying our submission to the active speaker detection task (task B-2) of ActivityNet Challenge 2019.
Ranked #16 on
Audio-Visual Active Speaker Detection
on AVA-ActiveSpeaker
(using extra training data)
Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition
Multi-view facial expression recognition (FER) is a challenging task because the appearance of an expression varies in poses.
Weakly Supervised Object Detection with Segmentation Collaboration
Weakly supervised object detection aims at learning precise object detectors, given image category labels.
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e. g. smartphone, embedded devices, etc.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning.
LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild
It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up.
Ranked #1 on
Lipreading
on LRW-1000
VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video
We also learn a deep HR estimator (named as RhythmNet) with the proposed spatial-temporal representation, which achieves promising results on both the public-domain and our VIPL-HR HR estimation databases.
Meta-Learning with Individualized Feature Space for Few-Shot Classification
Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images.
Generative Adversarial Network with Spatial Attention for Face Attribute Editing
The generator contains an attribute manipulation network (AMN) to edit the face image, and a spatial attention network (SAN) to localize the attribute-specific region which restricts the alternation of AMN within this region.
Face Recognition with Contrastive Convolution
In current face recognition approaches with convolutional neural network (CNN), a pair of faces to compare are independently fed into the CNN for feature extraction.
Facial Expression Recognition with Inconsistently Annotated Datasets
To address the inconsistency, we propose an Inconsistent Pseudo Annotations to Latent Truth(IPA2LT) framework to train a FER model from multiple inconsistently labeled datasets and large scale unlabeled data.
Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking
In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an end-to-end manner.
Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition
Zero-shot learning (ZSL) aims to recognize objects of novel classes without any training samples of specific classes, which is achieved by exploiting the semantic information and auxiliary datasets.
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
Context is important for accurate visual recognition.
Mean-Variance Loss for Deep Age Estimation From a Face
Age estimation has broad application prospects of many fields, such as video surveillance, social networking, and human-computer interaction.
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation
Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation.
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Rotation-invariant face detection, i. e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances.
Shift-Net: Image Inpainting via Deep Feature Rearrangement
To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts.
AttGAN: Facial Attribute Editing by Only Changing What You Want
Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes.
Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition
The designed ReST has an intrinsic recursive structure and is capable of progressively aligning faces to a canonical one, even those with large variations.
Learning Discriminative Latent Attributes for Zero-Shot Classification
Zero-shot learning (ZSL) aims to transfer knowledge from observed classes to the unseen classes, based on the assumption that both the seen and unseen classes share a common semantic space, among which attributes enjoy a great popularity.
Discriminative Covariance Oriented Representation Learning for Face Recognition With Image Sets
For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification.
Learning Multifunctional Binary Codes for Both Category and Attribute Oriented Retrieval Tasks
In this paper we propose a unified framework to address multiple realistic image retrieval tasks concerning both category and attributes.
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes.
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness
On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low time-cost.
VIPLFaceNet: An Open Source Deep Face Recognition SDK
Robust face representation is imperative to highly accurate face recognition.
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure.
Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video
With this mapping, the problem of learning a cross-view metric between the two source heterogeneous spaces can be expressed as learning a single-view Euclidean distance metric in the target common Euclidean space.
Self-paced Learning for Weakly Supervised Evidence Discovery in Multimedia Event Search
Multimedia event detection has been receiving increasing attention in recent years.
Dual Purpose Hashing
Recent years have seen more and more demand for a unified framework to address multiple realistic image retrieval tasks concerning both category and attributes.
Occlusion-Free Face Alignment: Deep Regression Networks Coupled With De-Corrupt AutoEncoders
Face alignment or facial landmark detection plays an important role in many computer vision applications, e. g., face recognition, facial expression recognition, face animation, etc.
Deep Supervised Hashing for Fast Image Retrieval
In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets.
Ranked #1 on
Image Retrieval
on CIFAR-10
Multi-View Deep Network for Cross-View Classification
As a result, the representation from the topmost layers of the MvDN network is robust to view discrepancy, and also discriminative.
Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation
To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE).
Leveraging Datasets With Varying Annotations for Face Alignment via Deep Regression Network
Facial landmark detection, as a vital topic in computer vision, has been studied for many decades and lots of datasets have been collected for evaluation.
A Unified Multiplicative Framework for Attribute Learning
Attributes are mid-level semantic properties of objects.
Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction
In this way, the learned binary codes can be applied to not only fine-grained face image retrieval, but also facial attributes prediction, which is the very innovation of this work, just like killing two birds with one stone.
Learning Mid-level Words on Riemannian Manifold for Action Recognition
Human action recognition remains a challenging task due to the various sources of video data and large intra-class variations.
Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition
3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local mode.
Cross-pose Face Recognition by Canonical Correlation Analysis
We argue that one of the diffculties in this problem is the severe misalignment in face images or feature vectors with different poses.
Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold
Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching.
Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition
Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial traits recognition tasks, including identity, age and gender classification.
Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition
In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold.
Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets
This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets.
Self-Paced Learning with Diversity
Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training.
Generalized Unsupervised Manifold Alignment
In this paper, we propose a generalized Unsupervised Manifold Alignment (GUMA) method to build the connections between different but correlated datasets without any known correspondences.
Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition
In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i. e. expressionlet.
Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification
Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively.
Adaptive Partial Differential Equation Learning for Visual Saliency Detection
Then by optimizing a discrete submodular function constrained with this LESD and a uniform matroid, the saliency seeds (i. e., boundary conditions) can be learnt for this image, thus achieving an optimal PDE system to model the evolution of visual saliency.
Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses
Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity.
Deeply Coupled Auto-encoder Networks for Cross-view Classification
In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers.
Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild
Spatial-Temporal Face Region Descriptor, STFRD) for images (resp.
