Search Results for author:
Found 31 papers, 7 papers with code
CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
They are insufficient to model both global and local structures of a given image, especially in small regions of tail classes.
CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation
First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views.
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal.
Micron-BERT: BERT-based Facial Micro-Expression Recognition
By incorporating these components into an end-to-end deep network, the proposed $\mu$-BERT significantly outperforms all previous work in various micro-expression tasks.
Ranked #1 on
Micro Expression Recognition
on SMIC
Micro Expression Recognition
Micro-Expression Recognition
+1
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach
The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car.
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
We therefore introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize faces of a subject given his/her extracted face recognition features.
Depth Perspective-aware Multiple Object Tracking
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions.
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results.
Ranked #1 on
Action Recognition
on Jester (Gesture Recognition)
The Right to Talk: An Audio-Visual Transformer Approach
Therefore, this work introduces a new Audio-Visual Transformer approach to the problem of localization and highlighting the main speaker in both audio and visual channels of a multi-speaker conversation video in the wild.
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
Semantic segmentation aims to predict pixel-level labels.
Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition
This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism.
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition
This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations.
Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.
Domain Generalization via Universal Non-volume Preserving Models
However, it has been largely overlooked in the problem of recognition in new unseen domains.
Image Alignment in Unseen Domains via Domain Deep Generalization
This paper presents a novel deep learning based approach to tackle the problem of across unseen modalities.
ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks
In addition, this work introduces a novel Angular Distillation Loss for distilling the feature direction and the sample distributions of the teacher's hypersphere to its student.
Fast Flow Reconstruction via Robust Invertible nxn Convolution
The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible $n \times n$ convolution helps to improve the performance of generative models significantly.
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models
However, it has been largely overlooked in the problem of recognition in new unseen domains.
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos
To demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on AffectNet database to benchmark the proposed EmoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii) examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos (GECV) dataset composed of 627 videos collected from publicly available sources.
MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
Deep neural networks have been widely used in numerous computer vision applications, particularly in face recognition.
Automatic Face Aging in Videos via Deep Reinforcement Learning
This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning.
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
Face Aging has raised considerable attentions and interest from the computer vision community in recent years.
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification.
Deep Contextual Recurrent Residual Networks for Scene Labeling
Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer vision problems.
Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages.
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling
This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations.
Robust Deep Appearance Models
In this approach, two crucial components of face images, i. e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively.
Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines
The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence.
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling
The "interpretation through synthesis", i. e.
