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Found 8 papers, 8 papers with code
Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology
In this paper, we present a new multigraph topology for cross-silo federated learning.
Deep Federated Learning for Autonomous Driving
We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods.
Coarse-to-Fine Reasoning for Visual Question Answering
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task.
Ranked #1 on
Visual Question Answering (VQA)
on GQA test-dev
Multiple Meta-model Quantifying for Medical Visual Question Answering
However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized.
Ranked #5 on
Medical Visual Question Answering
on PathVQA
Graph-based Person Signature for Person Re-Identifications
In this paper, we propose a new method to effectively aggregate detailed person descriptions (attributes labels) and visual features (body parts and global features) into a graph, namely Graph-based Person Signature, and utilize Graph Convolutional Networks to learn the topological structure of the visual signature of a person.
Ranked #48 on
Person Re-Identification
on DukeMTMC-reID
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering
Different approaches have been proposed to Visual Question Answering (VQA).
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space.
Overcoming Data Limitation in Medical Visual Question Answering
Traditional approaches for Visual Question Answering (VQA) require large amount of labeled data for training.
Ranked #13 on
Medical Visual Question Answering
on VQA-RAD
(using extra training data)
