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Found 12 papers, 3 papers with code
YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
2) Small object size leads to insufficient information for effective detection.
Heterogeneous Federated Learning with Splited Language Model
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradigms, to ensure model privacy while diminishing the resource overhead of each client, especially on large transformer models in a resource-constrained environment, e. g., Internet of Things (IoT).
Generic Knowledge Boosted Pre-training For Remote Sensing Images
Deep learning models are essential for scene classification, change detection, land cover segmentation, and other remote sensing image understanding tasks.
DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object Detection
Though feature-alignment based Domain Adaptive Object Detection (DAOD) have achieved remarkable progress, they ignore the source bias issue, i. e. the aligned features are more favorable towards the source domain, leading to a sub-optimal adaptation.
Incremental Object Detection with CLIP
In the incremental detection task, unlike the incremental classification task, data ambiguity exists due to the possibility of an image having different labeled bounding boxes in multiple continuous learning stages.
Context-Enhanced Detector For Building Detection From Remote Sensing Images
Our approach utilizes a three-stage cascade structure to enhance the extraction of contextual information and improve building detection accuracy.
Gate Recurrent Unit Network based on Hilbert-Schmidt Independence Criterion for State-of-Health Estimation
State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries.
SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Differentially private (DP) learning algorithms provide guarantees on identifying the existence of a training sample from model outputs.
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among $k$ parties.
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
We consider the problem of estimating the mean of a set of vectors, which are stored in a distributed system.
Communication-Efficient Weighted Sampling and Quantile Summary for GBDT
Gradient boosting decision tree (GBDT) is a powerful and widely-used machine learning model, which has achieved state-of-the-art performance in many academic areas and production environment.
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
In particular, on distributed ResNet training with 7 workers on the ImageNet, the proposed algorithm achieves the same testing accuracy as momentum SGD using full-precision gradients, but with $46\%$ less wall clock time.
