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Found 28 papers, 10 papers with code
Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information
Light-field cameras capture sub-views from multiple perspectives simultaneously, with possibly reflectance variations that can be used to augment material recognition in remote sensing, autonomous driving, etc.
Efficient Video Neural Network Processing Based on Motion Estimation
Video neural network (VNN) processing using the conventional pipeline first converts Bayer video information into human understandable RGB videos using image signal processing (ISP) on a pixel by pixel basis.
Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos
Snapshot compressive imaging (SCI) is a promising technique for capturing high-speed video at low bandwidth and low power, typically by compressing multiple frames into a single measurement.
Disentangling data distribution for Federated Learning
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy.
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval
Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and generalization.
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged.
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
In this paper, we introduce a Federated Domain-specific Knowledge Transfer (FDKT) framework, which enables domain-specific knowledge transfer from LLMs to SLMs while preserving clients' data privacy.
Gaussian Time Machine: A Real-Time Rendering Methodology for Time-Variant Appearances
In this paper, we present Gaussian Time Machine (GTM) which models the time-dependent attributes of Gaussian primitives with discrete time embedding vectors decoded by a lightweight Multi-Layer-Perceptron(MLP).
Leveraging Compressed Frame Sizes For Ultra-Fast Video Classification
Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially when an immense volume of video content is being constantly generated.
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy.
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention.
Judging a video by its bitstream cover
Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially in an age where an immense volume of video content is constantly being generated.
Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation
The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification.
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system.
Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos
In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS.
A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning
In this work, we propose a Federated Hybrid Self-Supervised Learning framework, named FedHSSL, that utilizes cross-party views (i. e., dispersed features) of samples aligned among parties and local views (i. e., augmentation) of unaligned samples within each party to improve the representation learning capability of the VFL joint model.
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.
Phocus: Picking Valuable Research from a Sea of Citations
The global influential factor of the reference to the citing paper is the product of the local influential factor and the total influential factor of the citing paper.
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.
A Unified Transferable Model for ML-Enhanced DBMS
We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.
Learning to compose 6-DoF omnidirectional videos using multi-sphere images
The system utilizes conventional omnidirectional VR camera footage directly without the need for a depth map or segmentation mask, thereby significantly simplifying the overall complexity of the 6-DoF omnidirectional video composition.
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
Recently proposed deep learning based methods largely improve the estimation accuracy but their performance can be greatly affected by data and often difficult for system deployment.
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications
To resolve this, we propose a new structure learning algorithm LEAST, which comprehensively fulfills our business requirements as it attains high accuracy, efficiency and scalability at the same time.
FSPN: A New Class of Probabilistic Graphical Model
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
Taming the Expressiveness and Programmability of Graph Analytical Queries
Graph database has enjoyed a boom in the last decade, and graph queries accordingly gain a lot of attentions from both the academia and industry.
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
In this paper we propose to use a denoising autoencoder (DAE) prior to simultaneously solve a linear inverse problem and estimate its noise parameter.
