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Found 27 papers, 8 papers with code
CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework
Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception.
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions
This tutorial seeks to provide an introduction to PFedRecSys, encompassing (1) an overview of existing studies on PFedRecSys, (2) a comprehensive taxonomy of PFedRecSys spanning four pivotal research directions-client-side adaptation, server-side aggregation, communication efficiency, privacy and protection, and (3) exploration of open challenges and promising future directions in PFedRecSys.
Learning to Hash for Recommendation: A Survey
In this survey, we present a comprehensive review of current HashRec algorithms.
Advancing Sustainability via Recommender Systems: A Survey
Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to transportation, energy utilization, and resource consumption collectively precipitating substantial ecological impacts.
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines
This paper investigates the efficacy of jointly optimizing content-specific post-processing filters to adapt a human oriented video/image codec into a codec suitable for machine vision tasks.
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Current methods mainly leverage aggregation functions invented by federated vision community to aggregate parameters from similar clients, e. g., clustering aggregation.
Navigating the Future of Federated Recommendation Systems with Foundation Models
In this study, we conduct a comprehensive review of FRSs with FMs.
TransFR: Transferable Federated Recommendation with Pre-trained Language Models
To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data.
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
To the best of our knowledge, this is the first research paper showing a hybrid video codec that outperforms VVC on multiple datasets and multiple machine vision tasks.
Bridging the gap between image coding for machines and humans
Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy.
SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR Point Clouds
Extensive experiments on three large-scale datasets reveal that our proposed SSC3OD can effectively improve the performance of sparsely supervised collaborative 3D object detectors.
Leveraging progressive model and overfitting for efficient learned image compression
Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H. 266) standard, due to either their compression performance or decoding complexity.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages.
Adaptation and Attention for Neural Video Coding
In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention.
Lossless Image Compression Using a Multi-Scale Progressive Statistical Model
Recently, multi-scale autoregressive models have been proposed to address this limitation.
Image coding for machines: an end-to-end learned approach
Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for those images.
Learned Image Coding for Machines: A Content-Adaptive Approach
One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption.
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.
Mask-GVAE: Blind Denoising Graphs via Partition
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.
Robust Data Hiding Using Inverse Gradient Attention
In deep data hiding models, to maximize the encoding capacity, each pixel of the cover image ought to be treated differently since they have different sensitivities w. r. t.
Dirichlet Graph Variational Autoencoder
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
Learning to Learn to Compress
In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance.
Adversarial Attack on Community Detection by Hiding Individuals
It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs.
Limited Random Walk Algorithm for Big Graph Data Clustering
Graph clustering is an important technique to understand the relationships between the vertices in a big graph.
Social and Information Networks Physics and Society
