11 research outputs found
Source-Guided Similarity Preservation for Online Person Re-Identification
Online Unsupervised Domain Adaptation (OUDA) for person Re-Identification
(Re-ID) is the task of continuously adapting a model trained on a
well-annotated source domain dataset to a target domain observed as a data
stream. In OUDA, person Re-ID models face two main challenges: catastrophic
forgetting and domain shift. In this work, we propose a new Source-guided
Similarity Preservation (S2P) framework to alleviate these two problems. Our
framework is based on the extraction of a support set composed of source images
that maximizes the similarity with the target data. This support set is used to
identify feature similarities that must be preserved during the learning
process. S2P can incorporate multiple existing UDA methods to mitigate
catastrophic forgetting. Our experiments show that S2P outperforms previous
state-of-the-art methods on multiple real-to-real and synthetic-to-real
challenging OUDA benchmarks.Comment: WACV 202
Time-varying Signals Recovery via Graph Neural Networks
The recovery of time-varying graph signals is a fundamental problem with
numerous applications in sensor networks and forecasting in time series.
Effectively capturing the spatio-temporal information in these signals is
essential for the downstream tasks. Previous studies have used the smoothness
of the temporal differences of such graph signals as an initial assumption.
Nevertheless, this smoothness assumption could result in a degradation of
performance in the corresponding application when the prior does not hold. In
this work, we relax the requirement of this hypothesis by including a learning
module. We propose a Time Graph Neural Network (TimeGNN) for the recovery of
time-varying graph signals. Our algorithm uses an encoder-decoder architecture
with a specialized loss composed of a mean squared error function and a Sobolev
smoothness operator.TimeGNN shows competitive performance against previous
methods in real datasets.Comment: Published in IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP) 2023, Greec
Inductive Graph Neural Networks for Moving Object Segmentation
Moving Object Segmentation (MOS) is a challenging problem in computer vision,
particularly in scenarios with dynamic backgrounds, abrupt lighting changes,
shadows, camouflage, and moving cameras. While graph-based methods have shown
promising results in MOS, they have mainly relied on transductive learning
which assumes access to the entire training and testing data for evaluation.
However, this assumption is not realistic in real-world applications where the
system needs to handle new data during deployment. In this paper, we propose a
novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on
a Graph Neural Network (GNN) architecture. Our approach builds a generic model
capable of performing prediction on newly added data frames using the already
trained model. GraphIMOS outperforms previous inductive learning methods and is
more generic than previous transductive techniques. Our proposed algorithm
enables the deployment of graph-based MOS models in real-world applications.Comment: Submitted to ICIP 202
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
International audienceGraph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains. Over-smoothing and over-squashing are key challenges when stacking graph convolutional layers, hindering deep representation learning and information propagation from distant nodes. Our work reveals that over-smoothing and over-squashing are intrinsically related to the spectral gap of the graph Laplacian, resulting in an inevitable trade-off between these two issues, as they cannot be alleviated simultaneously. To achieve a suitable compromise, we propose adding and removing edges as a viable approach. We introduce the Stochastic Jost and Liu Curvature Rewiring (SJLR) algorithm, which is computationally efficient and preserves fundamental properties compared to previous curvature-based methods. Unlike existing approaches, SJLR performs edge addition and removal during GNN training while maintaining the graph unchanged during testing. Comprehensive comparisons demonstrate SJLR's competitive performance in addressing over-smoothing and over-squashing. CCS CONCEPTS • Computing methodologies → Machine learning algorithms; • Computer systems organization → Neural networks
Estimation of a causal directed acyclic graph process using non-gaussianity
International audienceIn machine learning and data mining, causal relationship discovery is a critical task. While the state-of-the-art Vector Auto-Regressive Linear Non-Gaussian Acyclic Model (VAR-LiNGAM) method excels in uncovering both instantaneous and time-lagged connections, it entails analyzing multiple VAR matrices, leading to heightened parameter complexity. To address this challenge, we introduce the Causal Graph Process-LiNGAM (CGP-LiNGAM), a novel approach that significantly reduces parameter load by focusing on a single causal graph, a Directed Acyclic Graph (DAG). Leveraging Graph Signal Processing (GSP) techniques, our method interprets causal relations with graph shift invariance and uniqueness. Our experimental results demonstrate the superiority and robustness of CGP-LiNGAM, particularly in high-noise environments. Moreover, we showcase its real-world applicability in studying brain connectivity during sleep, underlining its compatibility with previous sleep-related neuroscientific research
Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation
Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this problem, we propose a new algorithm that uses HyperGraph Convolutional Networks for Weakly-supervised Semantic Segmentation (HyperGCN-WSS). Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs. Then, we train a specialized HyperGraph Convolutional Network (HyperGCN) architecture using some weak signals. The outputs of the HyperGCN are denominated pseudo-labels, which are later used to train a DeepLab model for semantic segmentation. HyperGCN-WSS is evaluated on the PASCAL VOC 2012 dataset for semantic segmentation, using scribbles or clicks as weak signals. Our algorithm shows competitive performance against previous methods
Higher-order Sparse Convolutions in Graph Neural Networks
International audienceGraph Neural Networks (GNNs) have been applied to many problems in computer sciences. Capturing higher-order relationships between nodes is crucial to increase the expressive power of GNNs. However, existing methods to capture these relationships could be infeasible for large-scale graphs. In this work, we introduce a new higher-order sparse convolution based on the Sobolev norm of graph signals. Our Sparse Sobolev GNN (S-SobGNN) computes a cascade of filters on each layer with increasing Hadamard powers to get a more diverse set of functions, and then a linear combination layer weights the embeddings of each filter. We evaluate S-SobGNN in several applications of semi-supervised learning. S-SobGNN shows competitive performance in all applications as compared to several state-of-the-art methods