Search Results for author:
Found 155 papers, 37 papers with code
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection
In this work, we ask: can diffusion models be used to generate adversarial examples to improve both visual quality and attack performance?
Attribute-Guided Encryption with Facial Texture Masking
In this paper, we propose Attribute Guided Encryption with Facial Texture Masking (AGE-FTM) that performs a dual manifold adversarial attack on FR systems to achieve both good visual quality and high black box attack success rates.
The 7th AI City Challenge
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.
MOST: Multiple Object localization with Self-supervised Transformers for object discovery
In this work, we present Multiple Object localization with Self-supervised Transformers (MOST) that uses features of transformers trained using self-supervised learning to localize multiple objects in real world images.
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels.
Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
The dataset is composed of both real and synthetic videos from seven gesture classes, and is intended to support the study of synthetic-to-real domain shift for video-based action recognition.
STEPs: Self-Supervised Key Step Extraction from Unlabeled Procedural Videos
We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and performance.
A Brief Survey on Person Recognition at a Distance
Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras.
DiffAlign : Few-shot learning using diffusion based synthesis and alignment
Our approach is also shown to be effective in the zero-shot classification setup
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates.
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images
Medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc.
VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment
Extensive experiments on a wide range of vision- and vision-language downstream tasks demonstrate the effectiveness of VoLTA on fine-grained applications without compromising the coarse-grained downstream performance, often outperforming methods using significantly more caption and box annotations.
Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition.
REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition
It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion.
PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images
We present Progressively Deblurring Radiance Field (PDRF), a novel approach to efficiently reconstruct high quality radiance fields from blurry images.
Scalable Vehicle Re-Identification via Self-Supervision
As Computer Vision technologies become more mature for intelligent transportation applications, it is time to ask how efficient and scalable they are for large-scale and real-time deployment.
Where in the World is this Image? Transformer-based Geo-localization in the Wild
Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem.
The 6th AI City Challenge
The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries.
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning.
Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking
Multi-camera vehicle tracking is one of the most complicated tasks in Computer Vision as it involves distinct tasks including Vehicle Detection, Tracking, and Re-identification.
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time.
Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach
Our algorithm then processes the labeled and un-labeled foreground regions differently, a common practice in semi-supervised methods.
EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images
To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting.
Max-Margin Contrastive Learning
Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence.
Ranked #95 on
Self-Supervised Image Classification
on ImageNet
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures.
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
In D&D, we train a teacher network on images from one category of an attribute; e. g. light skintone.
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Under JSTM, we develop novel adversarial attacks and defenses.
Mutual Adversarial Training: Learning together is better than going alone
In other words, we can make a weak model more robust with the help of a strong teacher model.
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks.
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces
Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a large-scale, multi-track visual kinship recognition evaluation.
Self-Denoising Neural Networks for Few Shot Learning
This architecture, which we call a Self-Denoising Neural Network (SDNN), can be applied easily to most modern convolutional neural architectures, and can be used as a supplement to many existing few-shot learning techniques.
Identification of Attack-Specific Signatures in Adversarial Examples
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks.
LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network
Face super-resolution is a challenging and highly ill-posed problem since a low-resolution (LR) face image may correspond to multiple high-resolution (HR) ones during the hallucination process and cause a dramatic identity change for the final super-resolved results.
Finding Facial Forgery Artifacts with Parts-Based Detectors
Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day.
A Synthesis-Based Approach for Thermal-to-Visible Face Verification
Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition
We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface.
To Boost or not to Boost: On the Limits of Boosted Neural Networks
Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak" hypotheses, each of which may be only moderately accurate.
Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions
Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation.
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
In contrast, the Hidden Trigger Backdoor Attack achieves poisoning without placing a trigger into the training data at all.
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images.
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset.
Cortical Features for Defense Against Adversarial Audio Attacks
We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio.
Certified Watermarks for Neural Networks
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors
A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane.
A study of quality and diversity in K+1 GANs
We study the K+1 GAN paradigm which generalizes the canonical true/fake GAN by training a generator with a K+1-ary classifier instead of a binary discriminator.
Pose And Joint-Aware Action Recognition
Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition.
Ranked #1 on
Action Recognition
on Mimetics
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
To remedy this issue, robust formulations of OT with unbalanced marginal constraints have previously been proposed.
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
To tackle this issue, we take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Using OM-ImageNet, we first show that adversarial training in the latent space of images improves both standard accuracy and robustness to on-manifold attacks.
Visual Question Answering on Image Sets
To enable research in this new topic, we introduce two ISVQA datasets - indoor and outdoor scenes.
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks.
Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition
Recognizing the expressions of partially occluded faces is a challenging computer vision problem.
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification
In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information.
Spatial Priming for Detecting Human-Object Interactions
The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object.
Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much higher than ever before.
cGANs with Multi-Hinge Loss
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings.
Ranked #6 on
Conditional Image Generation
on CIFAR-100
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
Additionally, we show how InvGAN can be used to implement reparameterization white-box attacks on projection-based defense mechanisms.
How are attributes expressed in face DCNNs?
In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw.
ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence
To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image.
Wasserstein Distance Based Domain Adaptation for Object Detection
Prior approaches utilize adversarial training based on cross entropy between the source and target domain distributions to learn a shared feature mapping that minimizes the domain gap.
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement
We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality.
Towards multi-sequence MR image recovery from undersampled k-space data
Undersampled MR image recovery has been widely studied for accelerated MR acquisition.
Landmark Detection in Low Resolution Faces with Semi-Supervised Learning
Landmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images.
DuDoNet: Dual Domain Network for CT Metal Artifact Reduction
The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training.
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images
Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation
Vehicle Re-Identification
Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition
In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph.
Detecting Human-Object Interactions via Functional Generalization
We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner.
3DRegNet: A Deep Neural Network for 3D Point Registration
Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame.
Unsupervised Domain-Specific Deblurring via Disentangled Representations
Image deblurring aims to restore the latent sharp images from the corresponding blurred ones.
On measuring the iconicity of a face
For a given identity in a face dataset, there are certain iconic images which are more representative of the subject than others.
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
Using the proposed normalized Wasserstein measure leads to significant performance gains for mixture distributions with imbalanced mixture proportions compared to the vanilla Wasserstein distance.
An Automatic System for Unconstrained Video-Based Face Recognition
In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames.
MetaReg: Towards Domain Generalization using Meta-Regularization
Training models that generalize to new domains at test time is a problem of fundamental importance in machine learning.
Ranked #47 on
Domain Generalization
on PACS
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
The deep regionlets framework consists of a region selection network and a deep regionlet learning module.
Recognizing Disguised Faces in the Wild
While upcoming algorithms continue to achieve improved performance, a majority of the face recognition systems are susceptible to failure under disguise variations, one of the most challenging covariate of face recognition.
Learning without Memorizing
Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model.
A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos
In this paper, we present a modular system for spatio-temporal action detection in untrimmed security videos.
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs).
A Fast and Accurate System for Face Detection, Identification, and Verification
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification
In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance.
Soft Sampling for Robust Object Detection
Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset.
Deep Density Clustering of Unconstrained Faces
In this paper, we consider the problem of grouping a collection of unconstrained face images in which the number of subjects is not known.
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Defense-GAN is trained to model the distribution of unperturbed images.
Zero-Shot Object Detection
We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training.
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning
These intermediate domains form a smooth path and bridge the gap between the source and target domains.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections.
Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment
Heatmap regression has been used for landmark localization for quite a while now.
Ranked #12 on
Face Alignment
on COFW
Task-Aware Compressed Sensing with Generative Adversarial Networks
We propose to train the GANs in a task-aware fashion, specifically for reconstruction tasks.
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention.
Semi-supervised FusedGAN for Conditional Image Generation
We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation.
Segment-based Methods for Facial Attribute Detection from Partial Faces
Taking several facial segments and the full face as input, the proposed method takes a data driven approach to determine which attributes are localized in which facial segments.
Deep Regionlets for Object Detection
The deep regionlets framework consists of a region selection network and a deep regionlet learning module.
Improving Network Robustness against Adversarial Attacks with Compact Convolution
In particular, we show that learning features in a closed and bounded space improves the robustness of the network.
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains.
A Deep Cascade Network for Unaligned Face Attribute Classification
First, a weakly-supervised face region localization network is designed to automatically detect regions (or parts) specific to attributes.
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity.
SSH: Single Stage Headless Face Detector
Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset.
Synthesis-based Robust Low Resolution Face Recognition
Recognition of low resolution face images is a challenging problem in many practical face recognition systems.
UPSET and ANGRI : Breaking High Performance Image Classifiers
In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods.
Hierarchical Multimodal Metric Learning for Multimodal Classification
The learned metrics can improve multimodal classification accuracy and experimental results on four datasets show that the proposed algorithm outperforms existing learning algorithms based on multiple metrics as well as other approaches tested on these datasets.
Regularizing deep networks using efficient layerwise adversarial training
In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training.
The Do's and Don'ts for CNN-based Face Verification
While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered.
Soft-NMS -- Improving Object Detection With One Line of Code
To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.
Partial Face Detection in the Mobile Domain
The three detectors following this approach, namely Facial Segment-based Face Detector (FSFD), SegFace and DeepSegFace, discussed in this paper, perform binary classification on each proposal based on features learned from facial segments.
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Domain Adaptation is an actively researched problem in Computer Vision.
Ranked #21 on
Domain Adaptation
on Office-31
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection
Different from existing approaches of modeling these relationships, we propose learnable transform functions which captures the relationships between keypoints at feature level.
L2-constrained Softmax Loss for Discriminative Face Verification
In recent years, the performance of face verification systems has significantly improved using deep convolutional neural networks (DCNNs).
Ranked #4 on
Face Verification
on IJB-A
A Proximity-Aware Hierarchical Clustering of Faces
In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations.
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors
In this paper, we show that without using any 3D information, KEPLER outperforms state of the art methods for alignment on challenging datasets such as AFW and AFLW.
Ranked #11 on
Head Pose Estimation
on BIWI
Learning from Ambiguously Labeled Face Images
To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance.
Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.
Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation
The third method works by first converting the continuous orientation estimation task into a set of discrete orientation estimation tasks and then converting the discrete orientation outputs back to the continuous orientation using a mean-shift algorithm.
Pooling Facial Segments to Face: The Shallow and Deep Ends
One promising technique to handle the challenge of partial faces is to design face detectors based on facial segments.
UMDFaces: An Annotated Face Dataset for Training Deep Networks
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets.
An All-In-One Convolutional Neural Network for Face Analysis
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.
Ranked #9 on
Face Verification
on IJB-A
PATH: Person Authentication using Trace Histories
In this paper, a solution to the problem of Active Authentication using trace histories is addressed.
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are investigated.
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images.
Ranked #1 on
Facial Expression Recognition (FER)
on CK+
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data
Then, using this representation, we model human actions as curves in this Lie group.
Ranked #4 on
Skeleton Based Action Recognition
on Gaming 3D (G3D)
DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size
Large-scale supervised classification algorithms, especially those based on deep convolutional neural networks (DCNNs), require vast amounts of training data to achieve state-of-the-art performance.
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
Convolutional Neural Networks for Attribute-based Active Authentication on Mobile Devices
We present a Deep Convolutional Neural Network (DCNN) architecture for the task of continuous authentication on mobile devices.
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification
Attributes, or semantic features, have gained popularity in the past few years in domains ranging from activity recognition in video to face verification.
Triplet Probabilistic Embedding for Face Verification and Clustering
Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.
Ranked #11 on
Face Verification
on IJB-A
Partial Face Detection for Continuous Authentication
In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed.
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN).
Ranked #2 on
Face Detection
on Annotated Faces in the Wild
Deep Feature-based Face Detection on Mobile Devices
We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front facing camera.
Optimized Kernel-based Projection Space of Riemannian Manifolds
Recently, it was shown that embedding such manifolds into a Random Projection Spaces (RPS), rather than RKHS or tangent space, leads to higher classification and clustering performance.
Triplet Similarity Embedding for Face Verification
In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods.
Face Alignment by Local Deep Descriptor Regression
We also present a face alignment algorithm based on regression using these local descriptors.
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection
Periodic inspections are necessary to keep railroad tracks in state of good repair and prevent train accidents.
Deep Multi-task Learning for Railway Track Inspection
Railroad tracks need to be periodically inspected and monitored to ensure safe transportation.
A Deep Pyramid Deformable Part Model for Face Detection
We present a face detection algorithm based on Deformable Part Models and deep pyramidal features.
Unconstrained Face Verification using Deep CNN Features
In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset.
Ranked #13 on
Face Verification
on IJB-A
Class Consistent Multi-Modal Fusion With Binary Features
Many existing recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time.
Submodular Attribute Selection for Action Recognition in Video
In real-world action recognition problems, low-level features cannot adequately characterize the rich spatial-temporal structures in action videos.
MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization
We experimentally demonstrate that the proposed MKL approach, which we refer to as MKL-RT, can be successfully used to select features for discriminative dimensionality reduction and cross-modal retrieval.
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
Recently introduced cost-effective depth sensors coupled with the real-time skeleton estimation algorithm of Shotton et al. have generated a renewed interest in skeleton-based human action recognition.
Ranked #6 on
Skeleton Based Action Recognition
on UT-Kinect
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information.
Growing Regression Forests by Classification: Applications to Object Pose Estimation
We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38. 5% and 22. 5% error reduction respectively).
Compositional Dictionaries for Domain Adaptive Face Recognition
This approach has three advantages: first, the extracted sparse representation for a subject is consistent across domains and enables pose and illumination insensitive face recognition.
Sparse Dictionary-based Attributes for Action Recognition and Summarization
We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes.
Generalized Domain-Adaptive Dictionaries
Data-driven dictionaries have produced state-of-the-art results in various classification tasks.
Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation
We present a hierarchical method for human pose estimation from a single still image.
Dictionary Learning from Ambiguously Labeled Data
We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label.
Kernel Learning for Extrinsic Classification of Manifold Features
In this paper, we address the issue of kernelselection for the classification of features that lie on Riemannian manifolds using the kernel learning approach.
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation
Domain adaptation addresses the problem where data instances of a source domain have different distributions from that of a target domain, which occurs frequently in many real life scenarios.
Compressive Acquisition of Dynamic Scenes
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate.
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds
In this paper, we consider the 'Precis' problem of sampling K representative yet diverse data points from a large dataset.
Large-Scale Matrix Factorization with Missing Data under Additional Constraints
We further demonstrate the effectiveness of the proposed algorithm in solving the affine SfM problem, non-rigid SfM and photometric stereo problems.
