1.1k citations · 4k across the 76 of their papers we have counts for
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Neural Belief Propagation for Scene Graph Generation
Daqi Liu, Miroslaw Bober, Josef Kittler
Scene graph generation aims to interpret an input image by explicitly modelling the potential objects and their relationships, which is predominantly solved by the message passing…
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning
Sara Atito, Muhammad Awais, Ammarah Farooq +2
Self-supervised pretraining is the method of choice for natural language processing models and is rapidly gaining popularity in many vision tasks. Recently, self-supervised pretrai…
Global Interaction Modelling in Vision Transformer via Super Tokens
Ammarah Farooq, Muhammad Awais, Sara Ahmed +1
With the popularity of Transformer architectures in computer vision, the research focus has shifted towards developing computationally efficient designs. Window-based local attenti…
Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms
Norman Poh, Thirimachos Bourlai, Josef Kittler +13
Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control…
Video Is Graph: Structured Graph Module for Video Action Recognition
Rongchang Li, Xiao-Jun Wu, Tianyang Xu
In the field of action recognition, video clips are always treated as ordered frames for subsequent processing. To achieve spatio-temporal perception, existing approaches propose t…
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Maria Perez-Ortiz, Omar Rivasplata, Benjamin Guedj +5
Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to l…
MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval
Donglin Zhang, Xiao-Jun Wu, He-Feng Yin +1
In recent years, cross-media hashing technique has attracted increasing attention for its high computation efficiency and low storage cost. However, the existing approaches still h…
UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation
D. Y. Rao, X. J. Wu, H. Li +2
In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content a…
PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion
Yu Fu, TianYang Xu, XiaoJun Wu +1
The Transformer architecture has witnessed a rapid development in recent years, outperforming the CNN architectures in many computer vision tasks, as exemplified by the Vision Tran…
SiT: Self-supervised vIsion Transformer
Sara Atito, Muhammad Awais, Josef Kittler
Self-supervised learning methods are gaining increasing traction in computer vision due to their recent success in reducing the gap with supervised learning. In natural language pr…
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
Hui Li, Xiao-Jun Wu, Josef Kittler
In the image fusion field, the design of deep learning-based fusion methods is far from routine. It is invariably fusion-task specific and requires a careful consideration. The mos…
NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition
Syed Safwan Khalid, Muhammad Awais, Chi-Ho Chan +4
Face recognition (FR) using deep convolutional neural networks (DCNNs) has seen remarkable success in recent years. One key ingredient of DCNN-based FR is the appropriate design of…
Differentiable Neural Architecture Learning for Efficient Neural Network Design
Qingbei Guo, Xiao-Jun Wu, Josef Kittler +1
Automated neural network design has received ever-increasing attention with the evolution of deep convolutional neural networks (CNNs), especially involving their deployment on emb…
Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion
Yu Fu, Xiao-Jun Wu, Josef Kittler
Image decomposition is a crucial subject in the field of image processing. It can extract salient features from the source image. We propose a new image decomposition method based…
Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training
Shuangping Jin, Zhenhua Feng, Wankou Yang +1
A big, diverse and balanced training data is the key to the success of deep neural network training. However, existing publicly available datasets used in facial landmark localizat…
AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification
Ammarah Farooq, Muhammad Awais, Josef Kittler +1
Cross-modal person re-identification (Re-ID) is critical for modern video surveillance systems. The key challenge is to align cross-modality representations induced by the semantic…