Search Results for author:
Found 132 papers, 73 papers with code
WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence
In spite of the success of pre-trained language model in many NLP tasks, the learned text representation only contains the correlation among the words in the sentence itself and ignores the implicit relationship between arbitrary tokens in the sequence.
Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction
In this work, we propose a new T2I personalization diffusion model, Dense-Face, which can generate face images with a consistent identity as the given reference subject and align well with the text caption.
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.
Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale Click-Through Rate Prediction at Taobao
In this work, we introduce a novel Multi-Branch Cooperation Network (MBCnet) which enables multiple branch networks to collaborate with each other for better complex feature interaction modeling.
Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization
We demonstrate the effectiveness of our method on $8$ by using different benchmarks for both tasks of IFDL and forgery attribute classification.
On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection
Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection.
Proactive Schemes: A Survey of Adversarial Attacks for Social Good
Adversarial attacks in computer vision exploit the vulnerabilities of machine learning models by introducing subtle perturbations to input data, often leading to incorrect predictions or classifications.
Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions.
Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine
The former fits DW images from diverse acquisition settings into diffusion tensor field, while the latter applies a deep learning-based denoiser to regularize the diffusion tensor field instead of the DW images, which is free from the limitation of fixed-channel assignment of the network.
COMPOSE: Comprehensive Portrait Shadow Editing
Existing portrait relighting methods struggle with precise control over facial shadows, particularly when faced with challenges such as handling hard shadows from directional light sources or adjusting shadows while remaining in harmony with existing lighting conditions.
RePLAy: Remove Projective LiDAR Depthmap Artifacts via Exploiting Epipolar Geometry
The KITTI dataset uses stereo cameras as a heuristic solution to remove artifacts.
Revisit Self-supervised Depth Estimation with Local Structure-from-Motion
Finally, we fix camera poses and employ a NeRF, however, without a neural network, for dense triangulation and geometric verification.
Open-Set Biometrics: Beyond Good Closed-Set Models
Biometric recognition has primarily addressed closed-set identification, assuming all probe subjects are in the gallery.
Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models
Recent advances in prompt optimization have notably enhanced the performance of pre-trained language models (PLMs) on downstream tasks.
StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
Large language models have shown their ability to become effective few-shot learners with prompting, revolutionizing the paradigm of learning with data scarcity.
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
We argue that the cause of failure is the sensitivity of depth regression losses to noise of larger objects.
3D Object Detection
3D Object Detection From Monocular Images
+3
KeyPoint Relative Position Encoding for Face Recognition
By anchoring the significance of pixels around keypoints, the model can more effectively retain spatial relationships, even when those relationships are disrupted by affine transformations.
ProMark: Proactive Diffusion Watermarking for Causal Attribution
ProMark can maintain image quality whilst outperforming correlation-based attribution.
BigGait: Learning Gait Representation You Want by Large Vision Models
Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities.
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications.
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Our experiments reveal that almost none of the existing detectors remain robust under all the attacks, and all detectors exhibit different loopholes.
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned methods by contrastive learning on selective perturbation.
Unified Physical-Digital Face Attack Detection
These three modules seamlessly form a robust unified attack detection framework.
Distilling CLIP with Dual Guidance for Learning Discriminative Human Body Shape Representation
Person Re-Identification (ReID) holds critical importance in computer vision with pivotal applications in public safety and crime prevention.
A Generalist FaceX via Learning Unified Facial Representation
This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously.
INFAMOUS-NeRF: ImproviNg FAce MOdeling Using Semantically-Aligned Hypernetworks with Neural Radiance Fields
We propose INFAMOUS-NeRF, an implicit morphable face model that introduces hypernetworks to NeRF to improve the representation power in the presence of many training subjects.
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
To explore such important dependencies, we propose a novel model parsing method called Learnable Graph Pooling Network (LGPN).
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
Improving Pacing in Long-Form Story Planning
Existing LLM-based systems for writing long-form stories or story outlines frequently suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details, resulting in a jarring experience for the reader.
ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation
This paper presents a novel approach, called Prototype-based Self-Distillation (ProS), for unsupervised face representation learning.
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification
Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics.
Ranked #2 on
Person Re-Identification
on CCVID
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective.
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance.
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
3D sensing for monocular in-the-wild images, e. g., depth estimation and 3D object detection, has become increasingly important.
DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.
Hierarchical Fine-Grained Image Forgery Detection and Localization
As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation.
PMatch: Paired Masked Image Modeling for Dense Geometric Matching
To resolve this, we reformulate the MIM from reconstructing a single masked image to reconstructing a pair of masked images, enabling the pretraining of transformer module.
MaLP: Manipulation Localization Using a Proactive Scheme
Finally, we show that MaLP can be used as a discriminator for improving the generation quality of GMs.
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations.
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
We call this 'model parsing of adversarial attacks' - a task to uncover 'arcana' in terms of the concealed VM information in attacks.
LightedDepth: Video Depth Estimation in Light of Limited Inference View Angles
This observation motivates us to decouple the video depth estimation into two components, a normalized pose estimation over a flowmap and a logged residual depth estimation over a mono-depth map.
Ranked #3 on
Monocular Depth Estimation
on KITTI Eigen split
Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects
The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner.
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning
Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently.
Cluster and Aggregate: Face Recognition with Large Probe Set
Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.
Ranked #1 on
Face Verification
on IJB-B
(TAR @ FAR=0.001 metric)
Multi-domain Learning for Updating Face Anti-spoofing Models
In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating.
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not.
3D Object Detection From Monocular Images
Monocular 3D Object Detection
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space.
Ranked #1 on
Face Verification
on IJB-S
2D GANs Meet Unsupervised Single-view 3D Reconstruction
In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images and perform the single-view reconstruction of generic objects.
AdaFace: Quality Adaptive Margin for Face Recognition
In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality.
Ranked #1 on
Surveillance-to-Booking
on IJB-S
Face Relighting with Geometrically Consistent Shadows
Most face relighting methods are able to handle diffuse shadows, but struggle to handle hard shadows, such as those cast by the nose.
Proactive Image Manipulation Detection
That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one.
Reverse Engineering of Imperceptible Adversarial Image Perturbations
However, carefully crafted, tiny adversarial perturbations are difficult to recover by optimizing a unilateral RED objective.
The State of Aerial Surveillance: A Survey
The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities.
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image
With complementary supervision from both 3D detection and reconstruction, one enables the 3D voxel features to be geometry and context preserving, benefiting both tasks. The effectiveness of our approach is demonstrated through 3D detection and reconstruction in single object and multiple object scenarios.
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation
Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis.
Full-Velocity Radar Returns by Radar-Camera Fusion
A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points.
Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints.
Radar-Camera Pixel Depth Association for Depth Completion
Here we propose a radar-to-pixel association stage which learns a mapping from radar returns to pixels.
Riggable 3D Face Reconstruction via In-Network Optimization
This paper presents a method for riggable 3D face reconstruction from monocular images, which jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations.
Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries
Key to our method is the use of an asymmetric loss function that operates on a novel twin-surface representation.
Unified Detection of Digital and Physical Face Attacks
Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94. 73% @ 0. 2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories.
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image.
Towards High Fidelity Face Relighting with Realistic Shadows
Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities.
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
In this paper, we present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection, such that the network is trained end-to-end with a loss on the boxes after NMS.
Ranked #11 on
3D Object Detection From Monocular Images
on KITTI-360
3D Object Detection From Monocular Images
Monocular 3D Object Detection
+2
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization
To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations.
Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder
While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing.
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template.
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
Additive process describes spoofing as spoof material introducing extra patterns (e. g., moire pattern), where the live counterpart can be recovered by removing those patterns.
Unify Local and Global Information for Top-$N$ Recommendation
To tackle this research gap, we propose a novel duet representation learning framework named \sysname to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-$N$ recommendation, which is composed of two separate sub-models.
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images
During training, FaceGuard automatically synthesizes challenging and diverse adversarial attacks, enabling a classifier to learn to distinguish them from real faces and a purifier attempts to remove the adversarial perturbations in the image space.
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner.
DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN
Robotic apple harvesting has received much research attention in the past few years due to growing shortage and rising cost in labor.
Small-floating Target Detection in Sea Clutter via Visual Feature Classifying in the Time-Doppler Spectra
It is challenging to detect small-floating object in the sea clutter for a surface radar.
Kinematic 3D Object Detection in Monocular Video
In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.
Ranked #6 on
3D Object Detection
on Rope3D
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed "spoof trace", e. g., color distortion, 3D mask edge, Moire pattern, and many others.
Mitigating Face Recognition Bias via Group Adaptive Classifier
Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes.
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities.
Ranked #1 on
Face Alignment
on Menpo
The Edge of Depth: Explicit Constraints between Segmentation and Depth
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images.
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability.
Ranked #13 on
Face Verification
on IJB-C
(TAR @ FAR=1e-4 metric)
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as the genuine user.
Camera Trace Erasing
To reconcile these two demands, we propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training.
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations
Graph convolution network (GCN) attracts intensive research interest with broad applications.
Improving Face Recognition from Hard Samples via Distribution Distillation Loss
To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.
FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization
FAN can leverage both paired and unpaired data as we disentangle the features into identity and non-identity components and adapt the distribution of the identity features, which breaks the limit of current face super-resolution methods.
Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
Our results show that object detection can help improve the accuracy of some skin disease classes.
Jointly De-biasing Face Recognition and Demographic Attribute Estimation
We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups.
On the Detection of Digital Face Manipulation
Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task.
On Learning Disentangled Representations for Gait Recognition
The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature.
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
Understanding the world in 3D is a critical component of urban autonomous driving.
3D Object Detection From Monocular Images
Autonomous Driving
+5
Gait Recognition via Disentangled Representation Learning
Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features.
Towards High-fidelity Nonlinear 3D Face Morphable Model
By improving the nonlinear 3D morphable model in both learning objective and network architecture, we present a model which is superior in capturing higher level of details than the linear or its precedent nonlinear counterparts.
Ranked #24 on
3D Face Reconstruction
on REALY
Deep Tree Learning for Zero-shot Face Anti-Spoofing
We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA).
Depth Coefficients for Depth Completion
We also show that the standard Mean Squared Error (MSE) loss function can promote depth mixing, and thus propose instead to use cross-entropy loss for DC.
3D Face Modeling From Diverse Raw Scan Data
Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database.
Pedestrian Detection with Autoregressive Network Phases
We present an autoregressive pedestrian detection framework with cascaded phases designed to progressively improve precision.
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation.
Recurrent Flow-Guided Semantic Forecasting
Understanding the world around us and making decisions about the future is a critical component to human intelligence.
On Learning 3D Face Morphable Model from In-the-wild Images
To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of in-the-wild face images, without collecting 3D face scans.
Face De-Spoofing: Anti-Spoofing via Noise Modeling
In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification.
Towards Interpretable Face Recognition
Deep CNNs have been pushing the frontier of visual recognition over past years.
Nonlinear 3D Face Morphable Model
As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e. g., model fitting, image synthesis.
Ranked #2 on
Face Alignment
on AFLW2000
Face Alignment in Full Pose Range: A 3D Total Solution
In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.
Ranked #3 on
Face Alignment
on AFLW
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously.
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
Face anti-spoofing is the crucial step to prevent face recognition systems from a security breach.
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data
In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples.
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels.
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i. e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement.
Do Convolutional Neural Networks Learn Class Hierarchy?
We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data.
Monocular Video-Based Trailer Coupler Detection Using Multiplexer Convolutional Neural Network
This paper presents an automated monocular-camera-based computer vision system for autonomous self-backing-up a vehicle towards a trailer, by continuously estimating the 3D trailer coupler position and feeding it to the vehicle control system, until the alignment of the tow hitch with the trailers coupler.
Dense Face Alignment
Face alignment is a classic problem in the computer vision field.
Ranked #2 on
Face Alignment
on AFLW-LFPA
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.
MemNet: A Persistent Memory Network for Image Restoration
We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.
Pose-Invariant Face Alignment with a Single CNN
Face alignment has witnessed substantial progress in the last decade.
Ranked #13 on
Facial Landmark Detection
on 300W
Spatio-Temporal Alignment of Non-Overlapping Sequences From Independently Panning Cameras
We identify and tackle a novel scenario of this problem referred to as Nonoverlapping Sequences (NOS).
Missing Modalities Imputation via Cascaded Residual Autoencoder
To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities.
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
The large pose discrepancy between two face images is one of the key challenges in face recognition.
Image Super-Resolution via Deep Recursive Residual Network
Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks; recursive learning is used to control the model parameters while increasing the depth.
Ranked #10 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Illuminating Pedestrians via Simultaneous Detection & Segmentation
When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector.
Ranked #20 on
Pedestrian Detection
on Caltech
Representation Learning by Rotating Your Faces
First, the encoder-decoder structure of the generator enables DR-GAN to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition.
Towards Large-Pose Face Frontalization in the Wild
Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments.
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM Networks
RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs.
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition
First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks.
Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting
Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e. g, face recognition and 3D face reconstruction.
Adaptive 3D Face Reconstruction From Unconstrained Photo Collections
Given a collection of "in-the-wild" face images captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of an individual along with albedo information.
Temporally Robust Global Motion Compensation by Keypoint-based Congealing
Global motion compensation (GMC) removes the impact of camera motion and creates a video in which the background appears static over the progression of time.
Face Alignment Across Large Poses: A 3D Solution
Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community.
Ranked #3 on
3D Face Reconstruction
on Florence
Pose-Invariant 3D Face Alignment
Face alignment aims to estimate the locations of a set of landmarks for a given image.
Unconstrained 3D Face Reconstruction
Second, by leveraging emerging face alignment techniques and our novel normal field-based Laplace editing, a combination of landmark constraints and photometric stereo-based normals drives our surface reconstruction.
Joint Multi-Leaf Segmentation, Alignment and Tracking from Fluorescence Plant Videos
First, leaf segmentation and alignment are applied on the last frame of a plant video to find a number of well-aligned leaf candidates.
SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process
Overlap is one of the characteristics of social networks, in which a person may belong to more than one social group.
Social and Information Networks Data Structures and Algorithms Physics and Society
