Search Results for author:
Found 16 papers, 3 papers with code
Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing
To address this, we propose a new interactive MIM method that can establish interactions between different tokens, which is particularly beneficial for object detection in remote sensing.
Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images
Object detection in Remote Sensing Images (RSI) is a critical task for numerous applications in Earth Observation (EO).
GlobalDoc: A Cross-Modal Vision-Language Framework for Real-World Document Image Retrieval and Classification
Visual document understanding (VDU) has rapidly advanced with the development of powerful multi-modal language models.
Ranked #20 on
Document Image Classification
on RVL-CDIP
MMFormer: Multimodal Transformer Using Multiscale Self-Attention for Remote Sensing Image Classification
To benefit the complementary information between heterogeneous data, we introduce a new Multimodal Transformer (MMFormer) for Remote Sensing (RS) image classification using Hyperspectral Image (HSI) accompanied by another source of data such as Light Detection and Ranging (LiDAR).
Identity Documents Authentication based on Forgery Detection of Guilloche Pattern
In this work, these two steps are combined together to achieve two objectives: (i) extracted features should have good anticollision (discriminative) capabilities to distinguish between a pair of identity documents belonging to different classes, (ii) checking out the conformity of the guilloche pattern of a given identity document and its similarity to the guilloche pattern of an authentic version of the same country.
VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification
Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task.
Ranked #19 on
Document Image Classification
on RVL-CDIP
ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection
Instead of using coarse image patches with single-scale as in ViT, we propose the Multi-scale Multi-Head Self-Attention (MsMHSA) architecture to accommodate multi-scale patch partitions of Q, K, V feature maps to the heads of transformer in a coarse-to-fine manner, which enables to learn a fine-grained representation to perform pixel-level discrimination for face PAD.
Exploring Multi-Tasking Learning in Document Attribute Classification
In this work, we adhere to explore a Multi-Tasking learning (MTL) based network to perform document attribute classification such as the font type, font size, font emphasis and scanning resolution classification of a document image.
MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis
Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture.
A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices
The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue.
Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face
Face recognition of realistic visual images has been well studied and made a significant progress in the recent decade.
Face Detection in Camera Captured Images of Identity Documents under Challenging Conditions
Benefiting from the advance of deep convolutional neural network approaches (CNNs), many face detection algorithms have achieved state-of-the-art performance in terms of accuracy and very high speed in unconstrained applications.
Dynamic Multi-Task Learning for Face Recognition with Facial Expression
This multi-task learning with dynamic weights also boosts of the performance on the different tasks comparing to the state-of-art methods with single-task learning.
Ranked #1 on
Facial Expression Recognition (FER)
on Oulu-CASIA
Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition
Rather than the visual images, the face recognition of the caricatures is far from the performance of the visual images.
FaceLiveNet+: A Holistic Networks For Face Authentication Based On Dynamic Multi-task Convolutional Neural Networks
This paper proposes a holistic multi-task Convolutional Neural Networks (CNNs) with the dynamic weights of the tasks, namely FaceLiveNet+, for face authentication.
