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Found 4 papers, 2 papers with code
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving
How to model action sequences so that one can further consider the effects of different action parameters in the scenario is the bottleneck of the problem.
L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing
To overcome these limitations, we propose a novel latent space factorization model, called L2M-GAN, which is learned end-to-end and effective for editing both local and global attributes.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning
First, data augmentations are introduced to both the support and query sets with each sample now being represented as an augmented embedding (AE) composed of concatenated embeddings of both the original and augmented versions.
