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Found 7 papers, 3 papers with code
Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection
Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles.
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains.
VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph
With the integration of 30 datasets and four popular CV tasks, we demonstrate its usefulness across various scenarios when working with CV pipelines.
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity
Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications.
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting
Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards explainability.
CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion
We present CQELS 2. 0, the second version of Continuous Query Evaluation over Linked Streams.
Fantastic Data and How to Query Them
It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI).
