Search Results for author:
Found 16 papers, 3 papers with code
HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification
Using our approach, more than 70% of the samples with compact hash codes exit early on the Market1501 dataset, saving 80% of the networks computational cost and improving over other hash-based methods by 60%.
Weakly Supervised Face and Whole Body Recognition in Turbulent Environments
Face and person recognition have recently achieved remarkable success under challenging scenarios, such as off-pose and cross-spectrum matching.
Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition
Our proposed framework is composed of modified networks for extracting the most correlated intermediate representations from off-pose thermal and frontal visible face imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss function comprised of cross-spectrum and pose-correction losses.
Understanding Cross Domain Presentation Attack Detection for Visible Face Recognition
Instead, we propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems.
A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset
Thermal face imagery, which captures the naturally emitted heat from the face, is limited in availability compared to face imagery in the visible spectrum.
Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification
Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning.
Cross-Domain Identification for Thermal-to-Visible Face Recognition
Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e. g., $L_2$ norm) which perform best when images from two different domains (e. g., visible and thermal) are co-registered and temporally synchronized.
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network
However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images.
Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis
Finally, a pre-trained VGG-Face network is leveraged to extract features from the synthesized image and the input visible image for verification.
Robust Multi-Modal Sensor Fusion: An Adversarial Approach
Exploiting complementary information from different sensors, we show that target detection and classification problems can greatly benefit from this fusion approach and result in a performance increase.
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences.
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks
Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures.
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i. e. transferring) knowledge from sensor data and enhancing low-resolution target detection.
Thermal to Visible Synthesis of Face Images using Multiple Regions
Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively.
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data
We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks.
Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces
Previous approaches utilize a two-step procedure (visible feature estimation and visible image reconstruction) to synthesize the visible image given the corresponding polarimetric thermal image.
