74 citations · 315 across the 165 of their papers we have counts for
27 papers · 1 filter
Orthogonal SVD Covariance Conditioning and Latent Disentanglement
Yue Song, Nicu Sebe, Wei Wang
Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities.…
Consistency-Aware Anchor Pyramid Network for Crowd Localization
Xinyan Liu, Guorong Li, Yuankai Qi +4
Crowd localization aims to predict the spatial position of humans in a crowd scenario. We observe that the performance of existing methods is challenged from two aspects: (i) ranki…
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization
Yuyang Zhao, Zhun Zhong, Na Zhao +2
Domain shift widely exists in the visual world, while modern deep neural networks commonly suffer from severe performance degradation under domain shift due to the poor generalizat…
Deep Unsupervised Key Frame Extraction for Efficient Video Classification
Hao Tang, Lei Ding, Songsong Wu +3
Video processing and analysis have become an urgent task since a huge amount of videos (e.g., Youtube, Hulu) are uploaded online every day. The extraction of representative key fra…
Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis
Hao Tang, Ling Shao, Philip H. S. Torr +1
We present a novel bipartite graph reasoning Generative Adversarial Network (BiGraphGAN) for two challenging tasks: person pose and facial image synthesis. The proposed graph gener…
Overlap-guided Gaussian Mixture Models for Point Cloud Registration
Guofeng Mei, Fabio Poiesi, Cristiano Saltori +3
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs i…
Wave equation for Sturm-Liouville operator with singular intermediate coefficient and potential
Michael Ruzhansky, Alibek Yeskermessuly
In this paper, we consider a wave equation on a bounded domain with a Sturm-Liouville operator with a singular intermediate coefficient and a singular potential. To obtain and eval…
Budget-Aware Pruning for Multi-Domain Learning
Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos +2
Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant…
Vision+X: A Survey on Multimodal Learning in the Light of Data
Ye Zhu, Yu Wu, Nicu Sebe +1
We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of…
Smooth image-to-image translations with latent space interpolations
Yahui Liu, Enver Sangineto, Yajing Chen +5
Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain. One important, desired characteristic of these transformation…
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
Yue Song, Nicu Sebe, Wei Wang
The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings. In this paper, we observe that the singular value distribut…
Facial Expression Translation using Landmark Guided GANs
Hao Tang, Nicu Sebe
We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an…
Rethinking the Learning Paradigm for Facial Expression Recognition
Weijie Wang, Bo Li, Nicu Sebe +1
Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually e…
Uncertainty-guided Source-free Domain Adaptation
Subhankar Roy, Martin Trapp, Andrea Pilzer +4
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data a…
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Jichao Zhang, Aliaksandr Siarohin, Yahui Liu +3
Generative Neural Radiance Fields (GNeRF)-based 3D-aware GANs have showcased remarkable prowess in crafting high-fidelity images while upholding robust 3D consistency, particularly…
Unsupervised Domain Adaptation for Video Transformers in Action Recognition
Victor G. Turrisi da Costa, Giacomo Zara, Paolo Rota +4
Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have acquired remarkable importance and popularity in computer vision. However, when compared to the extens…
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3
3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to impro…
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière +5
3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when…
Batch-efficient EigenDecomposition for Small and Medium Matrices
Yue Song, Nicu Sebe, Wei Wang
EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particular…
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality
Yue Song, Nicu Sebe, Wei Wang
Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities.…
Interaction Transformer for Human Reaction Generation
Baptiste Chopin, Hao Tang, Naima Otberdout +2
We address the challenging task of human reaction generation, which aims to generate a corresponding reaction based on an input action. Most of the existing works do not focus on g…
Unsupervised High-Resolution Portrait Gaze Correction and Animation
Jichao Zhang, Jingjing Chen, Hao Tang +5
This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotat…
Spatial Entropy as an Inductive Bias for Vision Transformers
Elia Peruzzo, Enver Sangineto, Yahui Liu +4
Recent work on Vision Transformers (VTs) showed that introducing a local inductive bias in the VT architecture helps reducing the number of samples necessary for training. However,…
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
Yue Song, Nicu Sebe, Wei Wang
The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-class variations are difficult to be captured. One notable research line uses the Global Covar…