Search Results for author:
Found 3 papers, 3 papers with code
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
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)
